Research Article Design and Simulation Analysis for...
10
Research Article Design and Simulation Analysis for Integrated Vehicle Chassis-Network Control System Based on CAN Network Wei Yu and Ning Sun College of Automobile and Transport Engineering, Nanjing Forestry University, Nanjing 210037, China Correspondence should be addressed to Wei Yu; [email protected] Received 30 March 2016; Revised 18 May 2016; Accepted 26 June 2016 Academic Editor: Antonio Fern´ andez-Caballero Copyright © 2016 W. Yu and N. Sun. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Due to the different functions of the system used in the vehicle chassis control, the hierarchical control strategy also leads to many kinds of the network topology structure. According to the hierarchical control principle, this research puts forward the integrated control strategy of the chassis based on supervision mechanism. e purpose is to consider how the integrated control architecture affects the control performance of the system aſter the intervention of CAN network. Based on the principle of hierarchical control and fuzzy control, a fuzzy controller is designed, which is used to monitor and coordinate the ESP, AFS, and ARS. And the IVC system is constructed with the upper supervisory controller and three subcontrol systems on the Simulink platform. e network topology structure of IVC is proposed, and the IVC communication matrix based on CAN network communication is designed. With the common sensors and the subcontrollers as the CAN network independent nodes, the network induced delay and packet loss rate on the system control performance are studied by simulation. e results show that the simulation method can be used for designing the communication network of the vehicle. 1. Introduction From the current development of the control system of vehi- cle chassis, integration and networking trend is very obvi- ous [1]. e architecture of system control and network has different degrees of influence on the stability of chassis control. Due to the different functions of the system used in the vehicle chassis control, the hierarchical control strategy also leads to many kinds of the network topology structure and the distribution of the system computing tasks. In the 80s of last century, the researchers began to decompose the complex chassis control problem into a number of subcontrol systems and then use a mechanism to coordinate the dynamic relationship between the subsystems to meet the control requirements. erefore, the research and discussion of the integrated control architecture of the chassis form [2–9] began to become the focus. As far as the integrated control strategy of vehicle chassis is concerned, numerous studies have shown that the hierar- chical control can effectively reduce the operation conflict between different functional subsystems,and quickly and effectively make the vehicle get the best performance. A large number of literatures [2–4] divide chassis control into different subcontrol systems according to the vertical, lateral, and normal control systems, and the integrated optimization control of the chassis is realized through the hierarchical control strategy. Li et al. put forward the integrated control structure of chassis based on the combination of the main loop and servo loop and discussed the problems of different directional force and force distribution of the chassis [6]. Chang and Gordon divided the chassis control system into three layers to achieve the active collision avoidance control [8]. Using the system architecture for the independent control units of the chassis of integrated control with upper coordinated control [10] can effectively adjust the collabora- tive work of control units, avoid the conflict of the controllers, and make the vehicle obtain the best running state. rough the analysis of the complex working conditions, the super- vision mechanism is used to coordinate the multiple control Hindawi Publishing Corporation Journal of Sensors Volume 2016, Article ID 7142739, 9 pages http://dx.doi.org/10.1155/2016/7142739
Transcript of Research Article Design and Simulation Analysis for...
Research ArticleDesign and Simulation Analysis forIntegrated Vehicle Chassis-Network Control SystemBased on CAN Network
Wei Yu and Ning Sun
College of Automobile and Transport Engineering Nanjing Forestry University Nanjing 210037 China
Correspondence should be addressed to Wei Yu yuwei505163com
Received 30 March 2016 Revised 18 May 2016 Accepted 26 June 2016
Academic Editor Antonio Fernandez-Caballero
Copyright copy 2016 W Yu and N Sun This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited
Due to the different functions of the system used in the vehicle chassis control the hierarchical control strategy also leads to manykinds of the network topology structure According to the hierarchical control principle this research puts forward the integratedcontrol strategy of the chassis based on supervision mechanismThe purpose is to consider how the integrated control architectureaffects the control performance of the system after the intervention of CAN network Based on the principle of hierarchical controland fuzzy control a fuzzy controller is designed which is used to monitor and coordinate the ESP AFS and ARS And the IVCsystem is constructed with the upper supervisory controller and three subcontrol systems on the Simulink platform The networktopology structure of IVC is proposed and the IVC communication matrix based on CAN network communication is designedWith the common sensors and the subcontrollers as the CAN network independent nodes the network induced delay and packetloss rate on the system control performance are studied by simulation The results show that the simulation method can be usedfor designing the communication network of the vehicle
1 Introduction
From the current development of the control system of vehi-cle chassis integration and networking trend is very obvi-ous [1] The architecture of system control and networkhas different degrees of influence on the stability of chassiscontrol Due to the different functions of the system used inthe vehicle chassis control the hierarchical control strategyalso leads to many kinds of the network topology structureand the distribution of the system computing tasks In the80s of last century the researchers began to decompose thecomplex chassis control problem into a number of subcontrolsystems and then use amechanism to coordinate the dynamicrelationship between the subsystems to meet the controlrequirements Therefore the research and discussion of theintegrated control architecture of the chassis form [2ndash9]began to become the focus
As far as the integrated control strategy of vehicle chassisis concerned numerous studies have shown that the hierar-chical control can effectively reduce the operation conflict
between different functional subsystemsand quickly andeffectively make the vehicle get the best performance Alarge number of literatures [2ndash4] divide chassis control intodifferent subcontrol systems according to the vertical lateraland normal control systems and the integrated optimizationcontrol of the chassis is realized through the hierarchicalcontrol strategy Li et al put forward the integrated controlstructure of chassis based on the combination of the mainloop and servo loop and discussed the problems of differentdirectional force and force distribution of the chassis [6]
Chang and Gordon divided the chassis control systeminto three layers to achieve the active collision avoidancecontrol [8] Using the system architecture for the independentcontrol units of the chassis of integrated control with uppercoordinated control [10] can effectively adjust the collabora-tive work of control units avoid the conflict of the controllersand make the vehicle obtain the best running state Throughthe analysis of the complex working conditions the super-vision mechanism is used to coordinate the multiple control
Hindawi Publishing CorporationJournal of SensorsVolume 2016 Article ID 7142739 9 pageshttpdxdoiorg10115520167142739
2 Journal of Sensors
systems of the vehicle chassis which can achieve a very goodcontrol effect of the system integration [11]
For these reasons this paper firstly according to the hier-archical control principle puts forward integrated controlstrategy of the chassis based on supervision mechanismBased on the verification of the validity of this controlstrategy the purpose of the study is to consider how the inte-grated control architecture affects the control performanceof the system after the intervention of CAN network Someexploratory simulation research is carried out In order tofacilitate the discussion the integrated control system ofnetwork of the vehicle chassis based on network communi-cation is abbreviated as IVC-NCS namely Integrated VehicleChassis-Network Control System
2 Dynamic Model of the Vehicle
At present international vehicle coordinate system mainlyhas two kinds [12] one is SAE vehicle coordinate systemissued by American Society of Automotive Engineers andanother one is ISO vehicle coordinate system issued by Inter-national Standardization Organization In this paper SAEvehicle coordinate system is used for modeling calculationand analysis of vehicle dynamics Based on the above assump-tions the nonlinear vehicle dynamicsmodel has eight degreesof freedom
There are a lot of tire models to calculate the complexnonlinear force between the road surface and the wheel Themost commonly used in the project is magic formula raisedby Pacejka ofHolland [13 14] and unified tiremodel of overallconditions raised by Guo Konhui of China [15] This paperuses Dugoff tire model [16] which is often used in computersimulation It belongs to analyticalmodel and the parametersare small and easy to obtain
3 Architecture of IVC-NCS Based onSupervision Mechanism
Figure 1 shows the architecture of IVC-NCS based on super-vision mechanism Three subcontrol systems are ESP AFSand ARS Each subsystem can be controlled according to thecalculation of local state variables Based on the global stateof the vehicle the upper supervision controller judges thefunction weight by the vehicle stability for each subcontrolsystems The implementation of the execution mechanism isdetermined by the calculation results of each subcontrollerand control weight
31 ESP Subcontrol System ESP takes the handling stabilityas the control target on the critical conditions of the wholevehicle By controlling the braking intensity of four wheelsthe electronic control of vehicle active safety is finished Theyaw rate tracking is the control target by applying a brakingforce at the right wheel to correct the unstable state of thevehicleThe system adopts sliding mode control strategy andthe tracking error of yaw rate is defined as sliding modevariable
119904119887 = 119903 minus 119903idl (1)
where 119903 is the actual yaw rate and 119903idl is the ideal yaw rate
Supervisorycontroller
AFSsubcontroller
ESPsubcontroller
ARS sub-controller
Controlweight
Local variable Global state variable
Controlquantity
Controlquantity
Controlquantity
Local variableLocal variable
Figure 1 Architecture of IVC-NCS based on supervision mecha-nism
The condition for reaching the sliding surface is definedas
119904119887 = minus120582119887119904119887 minus 120581119887 sat(119904119887
120576119887
) (2)
where 120582119887 and 120581119887 are all positive constants 120582119887 reflects theresponse speed of yaw tracking controller 120581119887 shows the con-vergence rate of sliding mode surface of the system 119878119887 is thetracking error of yaw rate and 120576119887 is the thickness of boundarylayer
The sliding mode controller satisfies the stability condi-tion of Lyapunov sense
Ignoring the inclination of vehicle and considering for-mula (2) when ESP control system acts on braking of singlevehicle the calculation formula of additional yaw rate torqueis gotten
119872119911119888
119868119911119911
= minus
119871119891 (119865119910fl + 119865119910fr) minus 119871119903 (119865119910rl + 119865119910rr)
119868119911119911
+ 119903idl
minus 120582119887119904119887 minus 120581119887 sat(119904119887
120576119887
)
(3)
where 119872119911119888 is the additional yaw torque generated by longi-tudinal driving force or braking force 119868119911119911 is the moment ofinertia of the vehicle body around 119911-axis 119871119891 is the verticaldistance from the centroid to the front axle 119871119903 is thevertical distance from the centroid to the rear axle 119865119910fl is thelongitudinal force of the ground on the left front tire 119865119910fr isthe longitudinal force of the ground on the right front tire119865119910rlis the longitudinal force of the ground on the left rear tire119865119910rris the longitudinal force of the ground on the right rear tire119903idl is the ideal yaw rate 120582119887 reflects the response speed of yawtracking controller 120581119887 shows the convergence rate of slidingmode surface of the system 119878119887 is the tracking error of yawrate and 120576119887 is the thickness of boundary layer
In order to improve the unstable state in extreme con-ditions braking force is applied to inward rear wheel whenthe vehicle has the understeer or braking force is applied tooutward front wheel when the vehicle has the oversteer Itcan quickly and effectively improve vehicle stability There-fore the additional yaw torque calculated by formula (3) isconverted to the equivalent braking force that can be appliedto wheel with the most effective braking force
Journal of Sensors 3
32 AFS Subcontrol System In steering system of the vehiclechassis a relatively independent subcontrol system such asAFS is increased to adjust the front wheel angle for obtainingthe optimum performance of IVC-NCS
The system adopts sliding mode control strategy and thetracking error of yaw rate is defined as sliding mode variable
119904119904119891 = 119903 minus 119903idl (4)
where 119903 is the actual yaw rate and 119903idl is the ideal yaw rateThe condition for reaching the sliding surface is defined
as
119904119904119891 = minus120582119904119891119904119904119891 minus 120581119904119891 sat(119904119904119891
120576119904119891
) (5)
where
sat(119904119904119891
120576119904119891
) =
119904119904119891
120576119904119891
10038161003816100381610038161003816119904119904119891
10038161003816100381610038161003816lt 120576119904119891
sign(
119904119904119891
120576119904119891
)
10038161003816100381610038161003816119904119904119891
10038161003816100381610038161003816ge 120576119904119891
(6)
where 120582119904119891 and 120581119904119891 are all positive constants 120582119904119891 reflects theresponse speed of yaw tracking controller 120581119904119891 shows theconvergence rate of sliding mode surface of the system 119904119904119891is the tracking error of yaw rate and 120576119904119891 is the thickness ofboundary layer
The control law of steering angle about front wheel is
120575119891 =
1
11988721
[minus11988621119881119910 minus 11988622119903 + 119903idl minus 120582119891 (119903 minus 119903idl)]
minus 120581119891 sat(119904119904119891
120576119904119891
)
(7)
where 119881119910 is the lateral vehicle speed 119903 is the actual yaw rate119903idl is the ideal yaw rate 120582119891 reflects the response speed of yawtracking controller 120581119891 shows the convergence rate of slidingmode surface of the system 119904119904119891 is the tracking error of yawrate and 120576119904119891 is the thickness of boundary layer
According to the vehicle model of two degree of freedom11988721 = 2119871119891119862119891119868119911119911 where 119871119891 is the vertical distance from thecentroid to the front axle 119862119891 is the pitch damping 119868119911119911 is themoment of inertia of the vehicle body around 119885 axis
And11988621 = (2119871119903119862119903minus2119871119891119862119891)119868119911119911119881119909 where119871119903 is the verticaldistance from the centroid to the rear axle 119862119903 is the casterdamping 119871119891 is the vertical distance from the centroid to thefront axle 119868119911119911 is the moment of inertia of the vehicle bodyaround 119911-axis 119881119909 is the longitudinal speed
And 11988622 = minus(21198712
119891119862119891 + 2119871
2
119903119862119903)119868119911119911119881119909 where 119871119891 is the
vertical distance from the centroid to the front axle 119862119891 is thepitch damping 119871119903 is the vertical distance from the centroidto the rear axle 119862119903 is the caster damping 119868119911119911 is the momentof inertia of the vehicle body around 119911-axis and 119881119909 is thelongitudinal speed
33 ARS Subcontrol System Active four-wheel steering tech-nology can improve the handling stability of the vehicle at
high speed and the controlling flexibility at low speed Theideal yaw rate calculated by vehicle model of two degrees offreedom is the tracked target So ARS takes round steeringangle as the controlled variable
The system adopts sliding mode control strategy and thetracking error of yaw rate is defined as sliding mode variable
119904119904119903 = 119903 minus 119903idl (8)
where 119903 is the actual yaw rate and 119903idl is the ideal yaw rateThe condition for reaching the sliding surface is defined
as
119904119904119903 = minus120582119904119903119904119904119903 minus 120581119904119903 sat(119904119904119903
120576119904119903
) (9)
where
sat(119904119904119903
120576119904119903
) =
119904119904119903
120576119904119903
1003816100381610038161003816119904119904119903
1003816100381610038161003816lt 120576119904119903
sign(
119904119904119903
120576119904119903
) 1003816100381610038161003816119904119904119903
1003816100381610038161003816ge 120576119904119903
(10)
where 120582119904119903 and 120581119904119903 are all positive constants 120582119904119903 reflects theresponse speed of yaw tracking controller 120581119904119903 shows theconvergence rate of sliding mode surface of the system 119904119904119903is the tracking error of yaw rate and 120576119904119903 is the thickness ofboundary layer
In order to restrain the shake of high frequency causedby frequent switching on the sliding surface 120576119904119903 is taken asthe thickness of the boundary layer 120582119904119903 reflects the responsespeed of yaw tracking controller and 120581119904119903 reflects the rate howthe system reaches the sliding surface
34 Upper Supervisory Controller Design The control idea ofthe supervisory controller is as follows judging the steadystate of the vehicle according to the stability factor distribut-ing the weight of the control function of three subcontrollersand coordinating the output of each subcontroller
Firstly the stability factor of front and rear wheels isdefined as [17]
SF119891 =
100381610038161003816100381610038161198881120572119891 + 1198882119891
10038161003816100381610038161003816 (11)
where SF119891 is the possibility that the front wheels come intothe slipping state and 120572119891 is the corresponding sideslip angleof the middle of the left and right wheels on the front axle
SF119903 =10038161003816100381610038161198881120572119903 + 1198882119903
1003816100381610038161003816 (12)
where SF119903 is the possibility that the rear wheels come into theslipping state and 120572119903 is the corresponding sideslip angle of themiddle of the left and right wheels on the rear axle
1198881 and 1198882 can be obtained by analyzing the relationshipbetween the phase plane and the steering stability of the tire[18]
SF119891 and SF119903 show the possibility of the correspondingwheel beginning to side The larger the value the bigger theside slipping possibility of corresponding wheel namely thesmaller the control margin provided by the wheel Con-versely the smaller the value the greater the effective strengthof corresponding wheel
4 Journal of Sensors
Table 1 Rules of fuzzy controller of IVC
SF119891
SF119903
119882AFS 119882ARS 119882ESP
S S B B SS MS B M SS MB B S SS B B S MSMS S M B SMS MS M M MSMS MB M S MSMS B M S MBMB S M B SMB MS M M SMB MB M M MSMB B S S MBB S S B MSB MS S M MSB MB S S MBB B S S B
Through repeated simulation tests when SF119891 and SF119903 areless than 07 the active steering control of the front and rearwheels can meet the requirements of vehicle stability WhenSF119891 or SF119903 is bigger than 13 the use of ESP can be moreeffective to correct the excessive or lack steering state whichcan keep the vehicle stable fast When SF119891 and SF119903 are in therange from 07 to 13 the wheels with smaller stability factorprovide a greater role in vehicle stability control Based onthis the design of fuzzy logic controller is designed as follows
The controller takes the stability factors of the frontand rare wheels such as SF119891 and SF119903 as the input Themembership functions are in the same range [0 2] and thefuzzy subset is SMSMBB as shown in Figure 2(a) Theoutputs of the controller are the control weights of threesubcontrollers whose range is [0 1]
The membership functions of AFS and ARS are thesame and fuzzy subset is DME as shown in Figure 2(b)The membership function of ESP subcontroller is shown inFigure 2(c) and fuzzy subset is SMSMBBThe collectionof letters is as follows S is small M is medium and B is big
Considering the actual application of the computationand real-time all variables of the membership functions areeasy to be calculated by the procedure such as trigonometricfunction or trapezoidal function Table 1 shows the inferencerules of fuzzy controller of IVC
4 Network Topology Design of IVC-NCS
According to system control strategy of IVC combined withthe control requirements of vehicle stability the followingseveral points are considered as the basis for the designActual limitations of vehicle space layout are as followsbecause CAN network agreement and the correspondinginternational standards limit the length of the branchesconnecting the nodes and communication trunks so networknodes in the actual space layout is one of the major consid-erations of network topology structure Such as ARSC and
AFSC they are divided into two control units to control thesystem separately which is helpful to connect the sensors andthe executing agency
Load capacity constraint of network communication is asfollows for IVC-NCS if all sensors controllers and actuatorsexist as independent network nodes and the network worksin 250Kbps rate of regulated by vehicle high speed networkof SAE only from the theoretical calculation of CAN com-munication capability its load capacity is difficult to meetthe control requirements While the communication speedis increased to 500Kbps the anti-interference ability of thenode will be poor so it is difficult to realize the high speedcommunication in the bad electromagnetic environment
Real-time requirements of subsystems are as followsthree subsystems of IVC-NCS are the relatively indepen-dent closed-loop control system ESP subsystem has higherrequest on real-time of wheel speed signals which requiresthe executing agencies to react quickly according to controlorders
The sensors necessary for many systems are designed asindependent network nodes The subcontrol systems adopttraditional point-to-point connection in the controllers sen-sors and executing agencies Its object is to obtain satisfactoryreal-time performance and reliability
Based on above analysis the network in Figure 3 isdesigned as IVC-NCS structure CAN network is taken asthe communicationmedium of the controller node and eachsubsystem is connected with the traditional method of pointto point Considering that ESP system has obvious effect forvehicle stability in extreme conditions the supervision andcontrol tasks of the system and the control calculation of ESPare assigned to one node
The sensor signals are the basis of the controller tojudge the state of the vehicle and control instructions Whenthe network communication load suddenly increases theprobability of signal loss of low level sensors will be sig-nificantly increased Therefore in order to ensure the real-time performance of the sensor signal transmission themessage priority of the sensor nodes is set higher to avoidthe message loss in the control cycle which leads to controlinstability Table 2 shows the communication matrix table ofIVC-NCS Messages Msg7 and Msg9 as the state messagesof executing agencies can help the controller nodes tounderstand the operation status of the system Because theydo not participate in the control calculation so the priority islow and the transmission cycle is relatively large
5 Simulation and Result Analysis
According to nonlinear vehicle model with eight degreesof freedom to calculate the state of the vehicle Simulinkplatform is used for simulation Before the performance ofIVC-NCS the IVC system is simulated and tested to verifythe effectiveness of the controller
51 Effectiveness Verification of IVC System Control In orderto verify the effectiveness of IVC system the sine curve andthe step curve with the maximum value 5 degrees (about0087 rad) of the vehicle steering wheel are input to simulate
Journal of Sensors 5
Table 2 Communication matrix table of IVC-NCS
Message name Message content(signal) Transmission node Message property Signal description
Priority Cycle Data domainMsg1 Interf Node 1 1 Pending 8 bytes Meaningless messageMsg2 S Yaw Acc Node 2 2 119875 = 5ms 6 bytes Yaw rate and lateral accelerationMsg3 S StrWhAgl Node 3 3 119875 = 5ms 4 bytes Steering wheel angleMsg4 S Vx Node 4 4 119875 = 5ms 4 bytes Longitudinal speedMsg5 Weight Node 4 7 AP 4 bytes Control weight of AFS and ARSMsg6 S ARS Node 5 5 119875 = 5ms 8 bytes Speed and rotation angle of rear wheelMsg7 D ARS M Node 5 8 119875 = 20ms 4 bytes Rear wheel motor statusMsg8 S AFS Node 6 6 119875 = 5ms 8 bytes Speed and rotation angle of front wheelMsg9 D AFS M Node 6 9 119875 = 20ms 4 bytes Front wheel motor status
S B
00
1MS MB
05
06 07 08 09 10 11 12 13 14 2
(a)
M BS
0
1
05
0 01 02 03 04 05 06 07 08 09 1
(b)
MS BS
0
1 MB
05
0 01 02 03 04 05 06 07 08 09 1
(c)
Figure 2 (a) SF119891 and SF119903 (b) Control weight 119882AFS 119882ARS of AFS and ARS (c) Control weight 119882ESP of ESP
Combinationsensor node
Node 2
Nonlinear vehicle model with eightdegrees of freedom
Rear wheelangle sensor
Rear wheelspeed sensor speed sensor
Vehicle
Rear wheelsteering drive
Rear wheelcontroller node(ARS) Node 5
Brake valve
Supervisorycontroller node(ESP) Node 4
Front wheelcontroller node(AFS) Node 6
speed sensorFront wheel Rear wheel
steering drive
CAN
sensor nodeSteering wheel angle
Node 3
Front wheelangle sensor
Interfering nodeNode 1
Figure 3 IVC-NCS network structure
6 Journal of Sensors
0 2 4 6minus01
0
01
Time (s) Time (s)0 02 04 06 08 1
0
005
01
Ang
le (r
ad)
Ang
le (r
ad)
(a)
0 1 2 3 4 5 6minus04
minus02
0
02
04
Yaw
rate
(rad
s)
Time (s)
Ideal value
No controlIn control
(b)
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
Yaw
rate
(rad
s)
Time (s)
Ideal value
No controlIn control
(c)
Figure 4 (a) Input curve of front wheel angle (b)The response curve of yaw rate of the steering wheel with sine angle input (c)The responsecurve of yaw rate of the steering wheel with step angle input
the tracking response of the vehicle under different inputyaw rates According to the transmission ratio of the steeringsystem the corresponding input curve of front wheel steeringangle is shown in Figure 4(a) The vehicle travels at a goodroad with a adhesion coefficient of 085 and the initial speedis 25ms
Figures 4(b) and 4(c) are the response curves of vehicleyaw rate at different angle inputs It can be seen that the yawrate of the controlled vehicle can quickly and effectively trackthe ideal value when compared with the system without thecontrol For the sine input the execution of the vehicle is anonstandard single lane change test At this time due to thecorrection function of angle changes of the front wheel soafter the apparent slip the yaw rate is settled in zero value asshown in Figure 4(b)
Under the step input of steering wheel angle in Fig-ure 4(c) the yaw rate of the vehicle without control cannottrack the ideal value which appears as the trend of diver-gence So the vehicle cannot achieve stable circular motionand rollover because of instabilityThe yaw rate of the vehiclewith controllers is good at tracking the ideal value Simulationresults show that the IVC system can effectively improvethe stability vehicle in critical conditions which verifies theeffectiveness of the designed control system
52 Simulation Analysis of IVC-NCS Based on CAN In orderto investigate the performance change of the designed IVCsystem after the CAN network is involved in the control
the stability of the vehicle was investigated using the samestep input of the steering wheel The initial speed is 25msand the road adhesion coefficient is 085 Considering thepractical application of CAN network with high speed thecommunication rate is set to 250Kbps Node sends only dataframes If the interfering nodes do not send any messagethe network load is about 84 when the maximum is filledWhen the interference nodes send the interference messageof high priority with 4ms cycle it can ensure that the networkload is close to 1 but less than the network bandwidth whichensures the system communication not to lose the frames
According to the assumptions and simulation conditionsFigure 5 shows the comparison curve of yaw rate trackingaboutCANnetwork communication and point-to-point con-nection Compared with point-to-point connection modethe IVC system with CAN network connection can quicklyand effectively track the ideal value under the conditionof good network environment without changing the steadystate of the control system It can be clearly seen that inthe part of the amplified image the network involves inthe control system which makes the yaw rate fluctuate withmicroamplitude The overshoot of control increases from31 of the point-to-point connection to 6 of the CANnetwork connection
In order to investigate the influence of different networkstate on the control performance of the system the trackingsimulation test of vehicle yaw rate is carried out for differentnetwork load and packet loss rate
Journal of Sensors 7
0 01 02 03 04 05 06 07 08 09 10
00501
01502
02503
03504
035 04 045 05 055032034036038
Yaw
rate
(rad
s)
Time (s)
Ideal value
CANPoint to point
Figure 5 The response curve of IVC yaw rate of CAN networkconnection
Figure 6 shows the response curve of different packet lossrates of IVC-NCS yaw rate In the simulation process theinterference nodes do not send the messages It can be seenthat when the packet loss rate is lower than 20 the dynamiccharacteristic of the system becomes bad In the packet lossrate of 5 and 20 the corresponding overshoots of thesystem are about 9 and 125 In 03 s after the step input offrontwheel ends the vehicle yaw rate can be stable to track theideal valueWhen the packet loss rate is less than 40 the yawrate of the vehicle can be finally stabilized at an ideal valueWhen the packet loss rate is more than 40 the yaw rate isobviously fluctuated in the ideal yaw rate tracking process At50 the overshoot of yaw rate increases rapidly to about 42the vehicle begins to sideslip
When the packet loss rate is up to 60 the vehicleyaw rate tracking is seriously lagging behind which cannotachieve stable circular motion The analysis shows that whenthe packet loss rate is low themessage transmission keep highsuccess rate The information of the sensors can be obtainedby control nodes in time so the controller works fast withlittle effect on the performance of system control With theincrease of packet loss rate the control instructions cannotbe timely generated and executed which makes the controlcycle become longer The status of executing agency cannotbe corrected in time The input of executing agency will betoo large or too small which causes the control to fail
Figure 7 shows that the interference nodes send themessages of highest priority in 4ms cycle and the networkload is close to 1 The long dashes are the response curve ofyaw rate of CAN network without the interference when thenetwork load is about 84 The short dashed lines dashed-dotted lines and bold dashed lines are separately responsecurves of yaw rate at 119905 different packet loss rates when theload is full
Under the condition tomeet the communication require-ments of the control system when the network load isclose to 1 the induced delay of the system is largest It canbe calculated when the network load increases from 84to nearly 100 and the overshoot increases from 6 to 7When the network load is 1 and packet loss rate is 30
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
Yaw
rate
(rad
s)
Time (s)
Ideal valuePoint to pointLoss 5Loss 20
Loss 40Loss 50Loss 60
Figure 6 The response curve of different packet loss rates
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
04 045 05 055 06034
036
038
04
Yaw
rate
(rad
s)
Time (s)
Ideal valuePoint to pointNo interference
No lossLoss 30Loss 50
Figure 7 The response curve of yaw rate with full load of networkcommunication
the overshoot of yaw rate is 157 Therefore although thenetwork load increases as long as network load can meetthe communication requirements of the control system thenetwork intervention only has little effect on the qualityof dynamic control which does not change the steadycharacteristics of the system The vehicle can achieve thestable circular motion within 03 s of the yaw rare input ofthe front wheel
When the communication network is fully loaded andthe packet loss rate is 50 the vehicle cannot completethe scheduled circular motion The yaw rate of the vehiclediverges to make the vehicle out of control The simulationresults show that when the network bandwidth meets theneeds of control system the effect of the network induceddelay of control system is very small and negligible And thenetwork packet loss will affect the performance of controlsystem seriously When the packet loss rate is up to 50 thesystem control performance will deteriorate significantly
8 Journal of Sensors
53 Stability and Coordination Analysis From the devel-opment of the vehicle chassis control system the trend ofintegration and network is very obvious The system controlarchitecture and the network architecture form have differenteffects on the stability control of the chassis In this paperthe design of the control system fully takes into account thestability of the chassis control performance
Because ABS is the basis for the realization of ESPand the latter needs to achieve the independent control ofbraking intensity about the four wheels so ABS is designedas an independent four-channel mode As one kind of thecontroller associated with safety and real-time the executionand controller of ABS usually adopt directly connectedmanner in order to reduce the information switching delayand ensure the safety and stability of the vehicle
The control target of ESP system is to control the stabilityof the vehicle in the extreme conditions through the controlof braking strength of four wheels to achieve the activesafety In order to improve the unstable state of the vehiclein extreme conditions applying the braking force on inwardrear wheel with the understeer or on the outward front wheelwith the oversteer can quickly and effectively improve thestability Taking into account that ESP system has the obviouseffect on the vehicle stability in extreme conditions thestudy will assign the supervision and control tasks and thecalculation of ESP control to one node
For the performance of network control system commu-nication real-time performance is the most important factoraffecting the control performance which can be expressedand measured by network delay The existence of networkdelay reduces the control performance of the system whichwill lead to the loss of stability of the stable control system
Especially in extreme conditions the change of thevehicle state is largerWhen a large number of control instruc-tions are lost the adjustment of the new and old controlinstructions is bound to increase because of the large numberof cycles which will increase the action range of the actuatorTherefore toomuch data packet loss is extremely unfavorablefor the stability control When the packet loss rate is lessthan a certain value only the system dynamic characteristicbecomesworse and the system stability is not changedWhenthe packet loss rate reaches the critical value the systemcontrol stability is close to the critical state
In addition through the simulation experiment we canknow that CAN network intervention did not significantlyaffect the stability of vehicle braking Therefore when CANnetwork communication environment is good the networkinduced delay of CAN network has a little influence on theperformance of the controller which indicates that the ABScontroller built in this research has strong robustness on asingle road
In the 80s of last century the researchers began to tryto decompose the complex chassis control problem into anumber of subcontrol systems and then use a mechanism tocoordinate the dynamic relationship between the subsys-tems to meet the control requirements Using the uppercoordinated control for the integrated control architectureof multiple independent control units of the vehicle chassiscan effectively adjust the collaborative work of the control
units avoid the conflict between the controllers and makethe vehicle obtain optimal running state
The supervision mechanism is based on a hierarchicalcontrol principle combinedwith fuzzy control logic to designa controller to supervise and coordinate ESP AFS and ARSThe target of the upper supervisory controller according tothe stability factor to judge vehicle steady state is to redistrib-ute the control weights of three subsystems and coordinatethe output of each subcontroller
The sensors necessary for many systems are designed asindependent network nodes The subcontrol systems adopttraditional point-to-point connection in the controllers sen-sors and executing agencies Its object is to obtain satisfactoryreal-time performance and coordination
6 Conclusions
In this paper the vehicle chassis control system is taken as theapplication of CAN network The target focuses on how thenetwork affects the control system The ABS ASC and IVCare simulated The main research contents and conclusionsare as follows
According to the control theory of sliding mode ESP andAFS subcontrollers are designed to track the ideal yaw rateBased on the principle of hierarchical control and fuzzy con-trol a fuzzy controller is designed which is used to monitorand coordinate the ESP AFS andARS And the IVC system isconstructed with the upper supervisory controller and threesubcontrol systems on the Simulink platform Comparedwith the point-to-point connection the system simulationof IVC-NCS shows that the control of the integrated controlsystem has good performance
According to the IVC based on the supervision mech-anism combined with the function of each subsystem thenetwork topology structure of IVC is proposed and theIVC communication matrix based on CAN network com-munication is designed With the common sensors andthe subcontrollers as the CAN network independent nodesthe network induced delay and packet loss rate on thesystem control performance are studied by simulation Thesimulation results show that the network does not lose frameand even if the network traffic load is close to 1 the networkintervention of IVC can only show the very small change ofthe dynamic quality of the system The network packet losshas a significant impact on the performance of the systemcontrol When the packet loss rate is less than 30 only thesystem dynamic performance becomes worse and the systemstability does not change When the packet loss rate is up to50 the system control stability is close to the critical stateand the vehicle is unstable
Competing Interests
The authors declare that they have no competing interests
Acknowledgments
This study was funded byThe Natural Science Foundation ofJiangsu Province (BK20130977)
Journal of Sensors 9
References
[1] T Gordon M Howell and F Brandao ldquoIntegrated controlmethodologies for road vehiclesrdquoVehicle System Dynamics vol40 no 1ndash3 pp 157ndash190 2003
[2] H Chou and B DrsquoAndrea-Novel ldquoGlobal vehicle control usingdifferential braking torques and active suspension forcesrdquoVehicle System Dynamics vol 43 no 4 pp 261ndash284 2005
[3] C B Chu and W W Chen ldquoVehicle chassis system based onlayered coordinated controlrdquo Chinese Journal of MechanicalEngineering vol 44 no 2 pp 157ndash162 2008
[4] H Zhu and W W Chen ldquoActive control of vehicle suspensionand steering system based on strategy hierarchyrdquo Chinese Jour-nal of Agricultural Machinery vol 39 no 10 pp 1ndash6 2008
[5] M J L Boada B L Boada A Munoz and V Diaz ldquoIntegratedcontrol of front-wheel steering and front braking forces on thebasis of fuzzy logicrdquo Proceedings of the Institution of MechanicalEngineers Part D Journal of Automobile Engineering vol 220no 3 pp 253ndash267 2006
[6] D Li X Shen and F Yu ldquoIntegrated vehicle chassis control witha mainservo-loop structurerdquo International Journal of Automo-tive Technology vol 7 no 7 pp 803ndash812 2006
[7] E J Bedner and H H Chen ldquoA supervisory control to managebrakes and four-wheel-steer systemsrdquo SAE Paper 2004-01-10592004
[8] S Chang and T J Gordon ldquoA flexible hierarchical model-basedcontrol methodology for vehicle active safety systemsrdquo VehicleSystem Dynamics vol 46 supplement 1 pp 63ndash75 2008
[9] N Kelling ldquoThe BRAKE projectmdashcentralized versus dis-tributed redundancy for brake-by-wire systemsrdquo SAE Paper2002-01-0266 SAE International 2002
[10] J X Wang Research of Integrated Control System of VehicleChassis Based on Multi Agent Southeast University NanjingChina 2010
[11] Y Zhang C L Yin and J W Zhang ldquoA real time estimationmethod for the lateral velocity of the center of mass of thevehiclerdquo Chinese Journal of Mechanical Engineering vol 44 no2 pp 219ndash222 2008
[12] D Crolla and Y Fan Vehicle Dynamics and Control ChineseCommunications Press Beijing China 2003
[13] H B Pacejka andE Bakker ldquoMagic formula tyremodelrdquoVehicleSystem Dynamics vol 21 no 1 pp 1ndash18 1993
[14] H B Pacejka and I J M Besselink ldquoMagic formula tyre modelwith transient propertiesrdquo Vehicle System Dynamics vol 27supplement 1 pp 234ndash249 1997
[15] K H Guo and L Ren ldquoA unifield semi-empirical tire modelwith higher accuracy and less parametersrdquo SAETechnical PaperSeries 1999-01-0785 SAE International 1999
[16] H Dugoff P S Fancher and L Segal ldquoTyre performance char-acteristics affecting vehicle response to steering and brakingcontrol inputsrdquo Final Report US National 1969
[17] W Jinxiang and C Nan ldquoResearch on supervisory controlbased integrated chassis control framework and its simulationrdquoTransactions of the Chinese Society of Agricultural Machineryvol 40 no 9 pp 1ndash6 2009
[18] S Inagaki I Kushiro and M Yamamoto ldquoAnalysis on vehiclestability in critical cornering using phase-plane methodrdquo inProceedings of the International Symposium on Advanced VehicleControl (AVEC rsquo94) pp 287ndash292 Tsukuba- Shi Japan 1994
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
2 Journal of Sensors
systems of the vehicle chassis which can achieve a very goodcontrol effect of the system integration [11]
For these reasons this paper firstly according to the hier-archical control principle puts forward integrated controlstrategy of the chassis based on supervision mechanismBased on the verification of the validity of this controlstrategy the purpose of the study is to consider how the inte-grated control architecture affects the control performanceof the system after the intervention of CAN network Someexploratory simulation research is carried out In order tofacilitate the discussion the integrated control system ofnetwork of the vehicle chassis based on network communi-cation is abbreviated as IVC-NCS namely Integrated VehicleChassis-Network Control System
2 Dynamic Model of the Vehicle
At present international vehicle coordinate system mainlyhas two kinds [12] one is SAE vehicle coordinate systemissued by American Society of Automotive Engineers andanother one is ISO vehicle coordinate system issued by Inter-national Standardization Organization In this paper SAEvehicle coordinate system is used for modeling calculationand analysis of vehicle dynamics Based on the above assump-tions the nonlinear vehicle dynamicsmodel has eight degreesof freedom
There are a lot of tire models to calculate the complexnonlinear force between the road surface and the wheel Themost commonly used in the project is magic formula raisedby Pacejka ofHolland [13 14] and unified tiremodel of overallconditions raised by Guo Konhui of China [15] This paperuses Dugoff tire model [16] which is often used in computersimulation It belongs to analyticalmodel and the parametersare small and easy to obtain
3 Architecture of IVC-NCS Based onSupervision Mechanism
Figure 1 shows the architecture of IVC-NCS based on super-vision mechanism Three subcontrol systems are ESP AFSand ARS Each subsystem can be controlled according to thecalculation of local state variables Based on the global stateof the vehicle the upper supervision controller judges thefunction weight by the vehicle stability for each subcontrolsystems The implementation of the execution mechanism isdetermined by the calculation results of each subcontrollerand control weight
31 ESP Subcontrol System ESP takes the handling stabilityas the control target on the critical conditions of the wholevehicle By controlling the braking intensity of four wheelsthe electronic control of vehicle active safety is finished Theyaw rate tracking is the control target by applying a brakingforce at the right wheel to correct the unstable state of thevehicleThe system adopts sliding mode control strategy andthe tracking error of yaw rate is defined as sliding modevariable
119904119887 = 119903 minus 119903idl (1)
where 119903 is the actual yaw rate and 119903idl is the ideal yaw rate
Supervisorycontroller
AFSsubcontroller
ESPsubcontroller
ARS sub-controller
Controlweight
Local variable Global state variable
Controlquantity
Controlquantity
Controlquantity
Local variableLocal variable
Figure 1 Architecture of IVC-NCS based on supervision mecha-nism
The condition for reaching the sliding surface is definedas
119904119887 = minus120582119887119904119887 minus 120581119887 sat(119904119887
120576119887
) (2)
where 120582119887 and 120581119887 are all positive constants 120582119887 reflects theresponse speed of yaw tracking controller 120581119887 shows the con-vergence rate of sliding mode surface of the system 119878119887 is thetracking error of yaw rate and 120576119887 is the thickness of boundarylayer
The sliding mode controller satisfies the stability condi-tion of Lyapunov sense
Ignoring the inclination of vehicle and considering for-mula (2) when ESP control system acts on braking of singlevehicle the calculation formula of additional yaw rate torqueis gotten
119872119911119888
119868119911119911
= minus
119871119891 (119865119910fl + 119865119910fr) minus 119871119903 (119865119910rl + 119865119910rr)
119868119911119911
+ 119903idl
minus 120582119887119904119887 minus 120581119887 sat(119904119887
120576119887
)
(3)
where 119872119911119888 is the additional yaw torque generated by longi-tudinal driving force or braking force 119868119911119911 is the moment ofinertia of the vehicle body around 119911-axis 119871119891 is the verticaldistance from the centroid to the front axle 119871119903 is thevertical distance from the centroid to the rear axle 119865119910fl is thelongitudinal force of the ground on the left front tire 119865119910fr isthe longitudinal force of the ground on the right front tire119865119910rlis the longitudinal force of the ground on the left rear tire119865119910rris the longitudinal force of the ground on the right rear tire119903idl is the ideal yaw rate 120582119887 reflects the response speed of yawtracking controller 120581119887 shows the convergence rate of slidingmode surface of the system 119878119887 is the tracking error of yawrate and 120576119887 is the thickness of boundary layer
In order to improve the unstable state in extreme con-ditions braking force is applied to inward rear wheel whenthe vehicle has the understeer or braking force is applied tooutward front wheel when the vehicle has the oversteer Itcan quickly and effectively improve vehicle stability There-fore the additional yaw torque calculated by formula (3) isconverted to the equivalent braking force that can be appliedto wheel with the most effective braking force
Journal of Sensors 3
32 AFS Subcontrol System In steering system of the vehiclechassis a relatively independent subcontrol system such asAFS is increased to adjust the front wheel angle for obtainingthe optimum performance of IVC-NCS
The system adopts sliding mode control strategy and thetracking error of yaw rate is defined as sliding mode variable
119904119904119891 = 119903 minus 119903idl (4)
where 119903 is the actual yaw rate and 119903idl is the ideal yaw rateThe condition for reaching the sliding surface is defined
as
119904119904119891 = minus120582119904119891119904119904119891 minus 120581119904119891 sat(119904119904119891
120576119904119891
) (5)
where
sat(119904119904119891
120576119904119891
) =
119904119904119891
120576119904119891
10038161003816100381610038161003816119904119904119891
10038161003816100381610038161003816lt 120576119904119891
sign(
119904119904119891
120576119904119891
)
10038161003816100381610038161003816119904119904119891
10038161003816100381610038161003816ge 120576119904119891
(6)
where 120582119904119891 and 120581119904119891 are all positive constants 120582119904119891 reflects theresponse speed of yaw tracking controller 120581119904119891 shows theconvergence rate of sliding mode surface of the system 119904119904119891is the tracking error of yaw rate and 120576119904119891 is the thickness ofboundary layer
The control law of steering angle about front wheel is
120575119891 =
1
11988721
[minus11988621119881119910 minus 11988622119903 + 119903idl minus 120582119891 (119903 minus 119903idl)]
minus 120581119891 sat(119904119904119891
120576119904119891
)
(7)
where 119881119910 is the lateral vehicle speed 119903 is the actual yaw rate119903idl is the ideal yaw rate 120582119891 reflects the response speed of yawtracking controller 120581119891 shows the convergence rate of slidingmode surface of the system 119904119904119891 is the tracking error of yawrate and 120576119904119891 is the thickness of boundary layer
According to the vehicle model of two degree of freedom11988721 = 2119871119891119862119891119868119911119911 where 119871119891 is the vertical distance from thecentroid to the front axle 119862119891 is the pitch damping 119868119911119911 is themoment of inertia of the vehicle body around 119885 axis
And11988621 = (2119871119903119862119903minus2119871119891119862119891)119868119911119911119881119909 where119871119903 is the verticaldistance from the centroid to the rear axle 119862119903 is the casterdamping 119871119891 is the vertical distance from the centroid to thefront axle 119868119911119911 is the moment of inertia of the vehicle bodyaround 119911-axis 119881119909 is the longitudinal speed
And 11988622 = minus(21198712
119891119862119891 + 2119871
2
119903119862119903)119868119911119911119881119909 where 119871119891 is the
vertical distance from the centroid to the front axle 119862119891 is thepitch damping 119871119903 is the vertical distance from the centroidto the rear axle 119862119903 is the caster damping 119868119911119911 is the momentof inertia of the vehicle body around 119911-axis and 119881119909 is thelongitudinal speed
33 ARS Subcontrol System Active four-wheel steering tech-nology can improve the handling stability of the vehicle at
high speed and the controlling flexibility at low speed Theideal yaw rate calculated by vehicle model of two degrees offreedom is the tracked target So ARS takes round steeringangle as the controlled variable
The system adopts sliding mode control strategy and thetracking error of yaw rate is defined as sliding mode variable
119904119904119903 = 119903 minus 119903idl (8)
where 119903 is the actual yaw rate and 119903idl is the ideal yaw rateThe condition for reaching the sliding surface is defined
as
119904119904119903 = minus120582119904119903119904119904119903 minus 120581119904119903 sat(119904119904119903
120576119904119903
) (9)
where
sat(119904119904119903
120576119904119903
) =
119904119904119903
120576119904119903
1003816100381610038161003816119904119904119903
1003816100381610038161003816lt 120576119904119903
sign(
119904119904119903
120576119904119903
) 1003816100381610038161003816119904119904119903
1003816100381610038161003816ge 120576119904119903
(10)
where 120582119904119903 and 120581119904119903 are all positive constants 120582119904119903 reflects theresponse speed of yaw tracking controller 120581119904119903 shows theconvergence rate of sliding mode surface of the system 119904119904119903is the tracking error of yaw rate and 120576119904119903 is the thickness ofboundary layer
In order to restrain the shake of high frequency causedby frequent switching on the sliding surface 120576119904119903 is taken asthe thickness of the boundary layer 120582119904119903 reflects the responsespeed of yaw tracking controller and 120581119904119903 reflects the rate howthe system reaches the sliding surface
34 Upper Supervisory Controller Design The control idea ofthe supervisory controller is as follows judging the steadystate of the vehicle according to the stability factor distribut-ing the weight of the control function of three subcontrollersand coordinating the output of each subcontroller
Firstly the stability factor of front and rear wheels isdefined as [17]
SF119891 =
100381610038161003816100381610038161198881120572119891 + 1198882119891
10038161003816100381610038161003816 (11)
where SF119891 is the possibility that the front wheels come intothe slipping state and 120572119891 is the corresponding sideslip angleof the middle of the left and right wheels on the front axle
SF119903 =10038161003816100381610038161198881120572119903 + 1198882119903
1003816100381610038161003816 (12)
where SF119903 is the possibility that the rear wheels come into theslipping state and 120572119903 is the corresponding sideslip angle of themiddle of the left and right wheels on the rear axle
1198881 and 1198882 can be obtained by analyzing the relationshipbetween the phase plane and the steering stability of the tire[18]
SF119891 and SF119903 show the possibility of the correspondingwheel beginning to side The larger the value the bigger theside slipping possibility of corresponding wheel namely thesmaller the control margin provided by the wheel Con-versely the smaller the value the greater the effective strengthof corresponding wheel
4 Journal of Sensors
Table 1 Rules of fuzzy controller of IVC
SF119891
SF119903
119882AFS 119882ARS 119882ESP
S S B B SS MS B M SS MB B S SS B B S MSMS S M B SMS MS M M MSMS MB M S MSMS B M S MBMB S M B SMB MS M M SMB MB M M MSMB B S S MBB S S B MSB MS S M MSB MB S S MBB B S S B
Through repeated simulation tests when SF119891 and SF119903 areless than 07 the active steering control of the front and rearwheels can meet the requirements of vehicle stability WhenSF119891 or SF119903 is bigger than 13 the use of ESP can be moreeffective to correct the excessive or lack steering state whichcan keep the vehicle stable fast When SF119891 and SF119903 are in therange from 07 to 13 the wheels with smaller stability factorprovide a greater role in vehicle stability control Based onthis the design of fuzzy logic controller is designed as follows
The controller takes the stability factors of the frontand rare wheels such as SF119891 and SF119903 as the input Themembership functions are in the same range [0 2] and thefuzzy subset is SMSMBB as shown in Figure 2(a) Theoutputs of the controller are the control weights of threesubcontrollers whose range is [0 1]
The membership functions of AFS and ARS are thesame and fuzzy subset is DME as shown in Figure 2(b)The membership function of ESP subcontroller is shown inFigure 2(c) and fuzzy subset is SMSMBBThe collectionof letters is as follows S is small M is medium and B is big
Considering the actual application of the computationand real-time all variables of the membership functions areeasy to be calculated by the procedure such as trigonometricfunction or trapezoidal function Table 1 shows the inferencerules of fuzzy controller of IVC
4 Network Topology Design of IVC-NCS
According to system control strategy of IVC combined withthe control requirements of vehicle stability the followingseveral points are considered as the basis for the designActual limitations of vehicle space layout are as followsbecause CAN network agreement and the correspondinginternational standards limit the length of the branchesconnecting the nodes and communication trunks so networknodes in the actual space layout is one of the major consid-erations of network topology structure Such as ARSC and
AFSC they are divided into two control units to control thesystem separately which is helpful to connect the sensors andthe executing agency
Load capacity constraint of network communication is asfollows for IVC-NCS if all sensors controllers and actuatorsexist as independent network nodes and the network worksin 250Kbps rate of regulated by vehicle high speed networkof SAE only from the theoretical calculation of CAN com-munication capability its load capacity is difficult to meetthe control requirements While the communication speedis increased to 500Kbps the anti-interference ability of thenode will be poor so it is difficult to realize the high speedcommunication in the bad electromagnetic environment
Real-time requirements of subsystems are as followsthree subsystems of IVC-NCS are the relatively indepen-dent closed-loop control system ESP subsystem has higherrequest on real-time of wheel speed signals which requiresthe executing agencies to react quickly according to controlorders
The sensors necessary for many systems are designed asindependent network nodes The subcontrol systems adopttraditional point-to-point connection in the controllers sen-sors and executing agencies Its object is to obtain satisfactoryreal-time performance and reliability
Based on above analysis the network in Figure 3 isdesigned as IVC-NCS structure CAN network is taken asthe communicationmedium of the controller node and eachsubsystem is connected with the traditional method of pointto point Considering that ESP system has obvious effect forvehicle stability in extreme conditions the supervision andcontrol tasks of the system and the control calculation of ESPare assigned to one node
The sensor signals are the basis of the controller tojudge the state of the vehicle and control instructions Whenthe network communication load suddenly increases theprobability of signal loss of low level sensors will be sig-nificantly increased Therefore in order to ensure the real-time performance of the sensor signal transmission themessage priority of the sensor nodes is set higher to avoidthe message loss in the control cycle which leads to controlinstability Table 2 shows the communication matrix table ofIVC-NCS Messages Msg7 and Msg9 as the state messagesof executing agencies can help the controller nodes tounderstand the operation status of the system Because theydo not participate in the control calculation so the priority islow and the transmission cycle is relatively large
5 Simulation and Result Analysis
According to nonlinear vehicle model with eight degreesof freedom to calculate the state of the vehicle Simulinkplatform is used for simulation Before the performance ofIVC-NCS the IVC system is simulated and tested to verifythe effectiveness of the controller
51 Effectiveness Verification of IVC System Control In orderto verify the effectiveness of IVC system the sine curve andthe step curve with the maximum value 5 degrees (about0087 rad) of the vehicle steering wheel are input to simulate
Journal of Sensors 5
Table 2 Communication matrix table of IVC-NCS
Message name Message content(signal) Transmission node Message property Signal description
Priority Cycle Data domainMsg1 Interf Node 1 1 Pending 8 bytes Meaningless messageMsg2 S Yaw Acc Node 2 2 119875 = 5ms 6 bytes Yaw rate and lateral accelerationMsg3 S StrWhAgl Node 3 3 119875 = 5ms 4 bytes Steering wheel angleMsg4 S Vx Node 4 4 119875 = 5ms 4 bytes Longitudinal speedMsg5 Weight Node 4 7 AP 4 bytes Control weight of AFS and ARSMsg6 S ARS Node 5 5 119875 = 5ms 8 bytes Speed and rotation angle of rear wheelMsg7 D ARS M Node 5 8 119875 = 20ms 4 bytes Rear wheel motor statusMsg8 S AFS Node 6 6 119875 = 5ms 8 bytes Speed and rotation angle of front wheelMsg9 D AFS M Node 6 9 119875 = 20ms 4 bytes Front wheel motor status
S B
00
1MS MB
05
06 07 08 09 10 11 12 13 14 2
(a)
M BS
0
1
05
0 01 02 03 04 05 06 07 08 09 1
(b)
MS BS
0
1 MB
05
0 01 02 03 04 05 06 07 08 09 1
(c)
Figure 2 (a) SF119891 and SF119903 (b) Control weight 119882AFS 119882ARS of AFS and ARS (c) Control weight 119882ESP of ESP
Combinationsensor node
Node 2
Nonlinear vehicle model with eightdegrees of freedom
Rear wheelangle sensor
Rear wheelspeed sensor speed sensor
Vehicle
Rear wheelsteering drive
Rear wheelcontroller node(ARS) Node 5
Brake valve
Supervisorycontroller node(ESP) Node 4
Front wheelcontroller node(AFS) Node 6
speed sensorFront wheel Rear wheel
steering drive
CAN
sensor nodeSteering wheel angle
Node 3
Front wheelangle sensor
Interfering nodeNode 1
Figure 3 IVC-NCS network structure
6 Journal of Sensors
0 2 4 6minus01
0
01
Time (s) Time (s)0 02 04 06 08 1
0
005
01
Ang
le (r
ad)
Ang
le (r
ad)
(a)
0 1 2 3 4 5 6minus04
minus02
0
02
04
Yaw
rate
(rad
s)
Time (s)
Ideal value
No controlIn control
(b)
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
Yaw
rate
(rad
s)
Time (s)
Ideal value
No controlIn control
(c)
Figure 4 (a) Input curve of front wheel angle (b)The response curve of yaw rate of the steering wheel with sine angle input (c)The responsecurve of yaw rate of the steering wheel with step angle input
the tracking response of the vehicle under different inputyaw rates According to the transmission ratio of the steeringsystem the corresponding input curve of front wheel steeringangle is shown in Figure 4(a) The vehicle travels at a goodroad with a adhesion coefficient of 085 and the initial speedis 25ms
Figures 4(b) and 4(c) are the response curves of vehicleyaw rate at different angle inputs It can be seen that the yawrate of the controlled vehicle can quickly and effectively trackthe ideal value when compared with the system without thecontrol For the sine input the execution of the vehicle is anonstandard single lane change test At this time due to thecorrection function of angle changes of the front wheel soafter the apparent slip the yaw rate is settled in zero value asshown in Figure 4(b)
Under the step input of steering wheel angle in Fig-ure 4(c) the yaw rate of the vehicle without control cannottrack the ideal value which appears as the trend of diver-gence So the vehicle cannot achieve stable circular motionand rollover because of instabilityThe yaw rate of the vehiclewith controllers is good at tracking the ideal value Simulationresults show that the IVC system can effectively improvethe stability vehicle in critical conditions which verifies theeffectiveness of the designed control system
52 Simulation Analysis of IVC-NCS Based on CAN In orderto investigate the performance change of the designed IVCsystem after the CAN network is involved in the control
the stability of the vehicle was investigated using the samestep input of the steering wheel The initial speed is 25msand the road adhesion coefficient is 085 Considering thepractical application of CAN network with high speed thecommunication rate is set to 250Kbps Node sends only dataframes If the interfering nodes do not send any messagethe network load is about 84 when the maximum is filledWhen the interference nodes send the interference messageof high priority with 4ms cycle it can ensure that the networkload is close to 1 but less than the network bandwidth whichensures the system communication not to lose the frames
According to the assumptions and simulation conditionsFigure 5 shows the comparison curve of yaw rate trackingaboutCANnetwork communication and point-to-point con-nection Compared with point-to-point connection modethe IVC system with CAN network connection can quicklyand effectively track the ideal value under the conditionof good network environment without changing the steadystate of the control system It can be clearly seen that inthe part of the amplified image the network involves inthe control system which makes the yaw rate fluctuate withmicroamplitude The overshoot of control increases from31 of the point-to-point connection to 6 of the CANnetwork connection
In order to investigate the influence of different networkstate on the control performance of the system the trackingsimulation test of vehicle yaw rate is carried out for differentnetwork load and packet loss rate
Journal of Sensors 7
0 01 02 03 04 05 06 07 08 09 10
00501
01502
02503
03504
035 04 045 05 055032034036038
Yaw
rate
(rad
s)
Time (s)
Ideal value
CANPoint to point
Figure 5 The response curve of IVC yaw rate of CAN networkconnection
Figure 6 shows the response curve of different packet lossrates of IVC-NCS yaw rate In the simulation process theinterference nodes do not send the messages It can be seenthat when the packet loss rate is lower than 20 the dynamiccharacteristic of the system becomes bad In the packet lossrate of 5 and 20 the corresponding overshoots of thesystem are about 9 and 125 In 03 s after the step input offrontwheel ends the vehicle yaw rate can be stable to track theideal valueWhen the packet loss rate is less than 40 the yawrate of the vehicle can be finally stabilized at an ideal valueWhen the packet loss rate is more than 40 the yaw rate isobviously fluctuated in the ideal yaw rate tracking process At50 the overshoot of yaw rate increases rapidly to about 42the vehicle begins to sideslip
When the packet loss rate is up to 60 the vehicleyaw rate tracking is seriously lagging behind which cannotachieve stable circular motion The analysis shows that whenthe packet loss rate is low themessage transmission keep highsuccess rate The information of the sensors can be obtainedby control nodes in time so the controller works fast withlittle effect on the performance of system control With theincrease of packet loss rate the control instructions cannotbe timely generated and executed which makes the controlcycle become longer The status of executing agency cannotbe corrected in time The input of executing agency will betoo large or too small which causes the control to fail
Figure 7 shows that the interference nodes send themessages of highest priority in 4ms cycle and the networkload is close to 1 The long dashes are the response curve ofyaw rate of CAN network without the interference when thenetwork load is about 84 The short dashed lines dashed-dotted lines and bold dashed lines are separately responsecurves of yaw rate at 119905 different packet loss rates when theload is full
Under the condition tomeet the communication require-ments of the control system when the network load isclose to 1 the induced delay of the system is largest It canbe calculated when the network load increases from 84to nearly 100 and the overshoot increases from 6 to 7When the network load is 1 and packet loss rate is 30
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
Yaw
rate
(rad
s)
Time (s)
Ideal valuePoint to pointLoss 5Loss 20
Loss 40Loss 50Loss 60
Figure 6 The response curve of different packet loss rates
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
04 045 05 055 06034
036
038
04
Yaw
rate
(rad
s)
Time (s)
Ideal valuePoint to pointNo interference
No lossLoss 30Loss 50
Figure 7 The response curve of yaw rate with full load of networkcommunication
the overshoot of yaw rate is 157 Therefore although thenetwork load increases as long as network load can meetthe communication requirements of the control system thenetwork intervention only has little effect on the qualityof dynamic control which does not change the steadycharacteristics of the system The vehicle can achieve thestable circular motion within 03 s of the yaw rare input ofthe front wheel
When the communication network is fully loaded andthe packet loss rate is 50 the vehicle cannot completethe scheduled circular motion The yaw rate of the vehiclediverges to make the vehicle out of control The simulationresults show that when the network bandwidth meets theneeds of control system the effect of the network induceddelay of control system is very small and negligible And thenetwork packet loss will affect the performance of controlsystem seriously When the packet loss rate is up to 50 thesystem control performance will deteriorate significantly
8 Journal of Sensors
53 Stability and Coordination Analysis From the devel-opment of the vehicle chassis control system the trend ofintegration and network is very obvious The system controlarchitecture and the network architecture form have differenteffects on the stability control of the chassis In this paperthe design of the control system fully takes into account thestability of the chassis control performance
Because ABS is the basis for the realization of ESPand the latter needs to achieve the independent control ofbraking intensity about the four wheels so ABS is designedas an independent four-channel mode As one kind of thecontroller associated with safety and real-time the executionand controller of ABS usually adopt directly connectedmanner in order to reduce the information switching delayand ensure the safety and stability of the vehicle
The control target of ESP system is to control the stabilityof the vehicle in the extreme conditions through the controlof braking strength of four wheels to achieve the activesafety In order to improve the unstable state of the vehiclein extreme conditions applying the braking force on inwardrear wheel with the understeer or on the outward front wheelwith the oversteer can quickly and effectively improve thestability Taking into account that ESP system has the obviouseffect on the vehicle stability in extreme conditions thestudy will assign the supervision and control tasks and thecalculation of ESP control to one node
For the performance of network control system commu-nication real-time performance is the most important factoraffecting the control performance which can be expressedand measured by network delay The existence of networkdelay reduces the control performance of the system whichwill lead to the loss of stability of the stable control system
Especially in extreme conditions the change of thevehicle state is largerWhen a large number of control instruc-tions are lost the adjustment of the new and old controlinstructions is bound to increase because of the large numberof cycles which will increase the action range of the actuatorTherefore toomuch data packet loss is extremely unfavorablefor the stability control When the packet loss rate is lessthan a certain value only the system dynamic characteristicbecomesworse and the system stability is not changedWhenthe packet loss rate reaches the critical value the systemcontrol stability is close to the critical state
In addition through the simulation experiment we canknow that CAN network intervention did not significantlyaffect the stability of vehicle braking Therefore when CANnetwork communication environment is good the networkinduced delay of CAN network has a little influence on theperformance of the controller which indicates that the ABScontroller built in this research has strong robustness on asingle road
In the 80s of last century the researchers began to tryto decompose the complex chassis control problem into anumber of subcontrol systems and then use a mechanism tocoordinate the dynamic relationship between the subsys-tems to meet the control requirements Using the uppercoordinated control for the integrated control architectureof multiple independent control units of the vehicle chassiscan effectively adjust the collaborative work of the control
units avoid the conflict between the controllers and makethe vehicle obtain optimal running state
The supervision mechanism is based on a hierarchicalcontrol principle combinedwith fuzzy control logic to designa controller to supervise and coordinate ESP AFS and ARSThe target of the upper supervisory controller according tothe stability factor to judge vehicle steady state is to redistrib-ute the control weights of three subsystems and coordinatethe output of each subcontroller
The sensors necessary for many systems are designed asindependent network nodes The subcontrol systems adopttraditional point-to-point connection in the controllers sen-sors and executing agencies Its object is to obtain satisfactoryreal-time performance and coordination
6 Conclusions
In this paper the vehicle chassis control system is taken as theapplication of CAN network The target focuses on how thenetwork affects the control system The ABS ASC and IVCare simulated The main research contents and conclusionsare as follows
According to the control theory of sliding mode ESP andAFS subcontrollers are designed to track the ideal yaw rateBased on the principle of hierarchical control and fuzzy con-trol a fuzzy controller is designed which is used to monitorand coordinate the ESP AFS andARS And the IVC system isconstructed with the upper supervisory controller and threesubcontrol systems on the Simulink platform Comparedwith the point-to-point connection the system simulationof IVC-NCS shows that the control of the integrated controlsystem has good performance
According to the IVC based on the supervision mech-anism combined with the function of each subsystem thenetwork topology structure of IVC is proposed and theIVC communication matrix based on CAN network com-munication is designed With the common sensors andthe subcontrollers as the CAN network independent nodesthe network induced delay and packet loss rate on thesystem control performance are studied by simulation Thesimulation results show that the network does not lose frameand even if the network traffic load is close to 1 the networkintervention of IVC can only show the very small change ofthe dynamic quality of the system The network packet losshas a significant impact on the performance of the systemcontrol When the packet loss rate is less than 30 only thesystem dynamic performance becomes worse and the systemstability does not change When the packet loss rate is up to50 the system control stability is close to the critical stateand the vehicle is unstable
Competing Interests
The authors declare that they have no competing interests
Acknowledgments
This study was funded byThe Natural Science Foundation ofJiangsu Province (BK20130977)
Journal of Sensors 9
References
[1] T Gordon M Howell and F Brandao ldquoIntegrated controlmethodologies for road vehiclesrdquoVehicle System Dynamics vol40 no 1ndash3 pp 157ndash190 2003
[2] H Chou and B DrsquoAndrea-Novel ldquoGlobal vehicle control usingdifferential braking torques and active suspension forcesrdquoVehicle System Dynamics vol 43 no 4 pp 261ndash284 2005
[3] C B Chu and W W Chen ldquoVehicle chassis system based onlayered coordinated controlrdquo Chinese Journal of MechanicalEngineering vol 44 no 2 pp 157ndash162 2008
[4] H Zhu and W W Chen ldquoActive control of vehicle suspensionand steering system based on strategy hierarchyrdquo Chinese Jour-nal of Agricultural Machinery vol 39 no 10 pp 1ndash6 2008
[5] M J L Boada B L Boada A Munoz and V Diaz ldquoIntegratedcontrol of front-wheel steering and front braking forces on thebasis of fuzzy logicrdquo Proceedings of the Institution of MechanicalEngineers Part D Journal of Automobile Engineering vol 220no 3 pp 253ndash267 2006
[6] D Li X Shen and F Yu ldquoIntegrated vehicle chassis control witha mainservo-loop structurerdquo International Journal of Automo-tive Technology vol 7 no 7 pp 803ndash812 2006
[7] E J Bedner and H H Chen ldquoA supervisory control to managebrakes and four-wheel-steer systemsrdquo SAE Paper 2004-01-10592004
[8] S Chang and T J Gordon ldquoA flexible hierarchical model-basedcontrol methodology for vehicle active safety systemsrdquo VehicleSystem Dynamics vol 46 supplement 1 pp 63ndash75 2008
[9] N Kelling ldquoThe BRAKE projectmdashcentralized versus dis-tributed redundancy for brake-by-wire systemsrdquo SAE Paper2002-01-0266 SAE International 2002
[10] J X Wang Research of Integrated Control System of VehicleChassis Based on Multi Agent Southeast University NanjingChina 2010
[11] Y Zhang C L Yin and J W Zhang ldquoA real time estimationmethod for the lateral velocity of the center of mass of thevehiclerdquo Chinese Journal of Mechanical Engineering vol 44 no2 pp 219ndash222 2008
[12] D Crolla and Y Fan Vehicle Dynamics and Control ChineseCommunications Press Beijing China 2003
[13] H B Pacejka andE Bakker ldquoMagic formula tyremodelrdquoVehicleSystem Dynamics vol 21 no 1 pp 1ndash18 1993
[14] H B Pacejka and I J M Besselink ldquoMagic formula tyre modelwith transient propertiesrdquo Vehicle System Dynamics vol 27supplement 1 pp 234ndash249 1997
[15] K H Guo and L Ren ldquoA unifield semi-empirical tire modelwith higher accuracy and less parametersrdquo SAETechnical PaperSeries 1999-01-0785 SAE International 1999
[16] H Dugoff P S Fancher and L Segal ldquoTyre performance char-acteristics affecting vehicle response to steering and brakingcontrol inputsrdquo Final Report US National 1969
[17] W Jinxiang and C Nan ldquoResearch on supervisory controlbased integrated chassis control framework and its simulationrdquoTransactions of the Chinese Society of Agricultural Machineryvol 40 no 9 pp 1ndash6 2009
[18] S Inagaki I Kushiro and M Yamamoto ldquoAnalysis on vehiclestability in critical cornering using phase-plane methodrdquo inProceedings of the International Symposium on Advanced VehicleControl (AVEC rsquo94) pp 287ndash292 Tsukuba- Shi Japan 1994
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
Journal of Sensors 3
32 AFS Subcontrol System In steering system of the vehiclechassis a relatively independent subcontrol system such asAFS is increased to adjust the front wheel angle for obtainingthe optimum performance of IVC-NCS
The system adopts sliding mode control strategy and thetracking error of yaw rate is defined as sliding mode variable
119904119904119891 = 119903 minus 119903idl (4)
where 119903 is the actual yaw rate and 119903idl is the ideal yaw rateThe condition for reaching the sliding surface is defined
as
119904119904119891 = minus120582119904119891119904119904119891 minus 120581119904119891 sat(119904119904119891
120576119904119891
) (5)
where
sat(119904119904119891
120576119904119891
) =
119904119904119891
120576119904119891
10038161003816100381610038161003816119904119904119891
10038161003816100381610038161003816lt 120576119904119891
sign(
119904119904119891
120576119904119891
)
10038161003816100381610038161003816119904119904119891
10038161003816100381610038161003816ge 120576119904119891
(6)
where 120582119904119891 and 120581119904119891 are all positive constants 120582119904119891 reflects theresponse speed of yaw tracking controller 120581119904119891 shows theconvergence rate of sliding mode surface of the system 119904119904119891is the tracking error of yaw rate and 120576119904119891 is the thickness ofboundary layer
The control law of steering angle about front wheel is
120575119891 =
1
11988721
[minus11988621119881119910 minus 11988622119903 + 119903idl minus 120582119891 (119903 minus 119903idl)]
minus 120581119891 sat(119904119904119891
120576119904119891
)
(7)
where 119881119910 is the lateral vehicle speed 119903 is the actual yaw rate119903idl is the ideal yaw rate 120582119891 reflects the response speed of yawtracking controller 120581119891 shows the convergence rate of slidingmode surface of the system 119904119904119891 is the tracking error of yawrate and 120576119904119891 is the thickness of boundary layer
According to the vehicle model of two degree of freedom11988721 = 2119871119891119862119891119868119911119911 where 119871119891 is the vertical distance from thecentroid to the front axle 119862119891 is the pitch damping 119868119911119911 is themoment of inertia of the vehicle body around 119885 axis
And11988621 = (2119871119903119862119903minus2119871119891119862119891)119868119911119911119881119909 where119871119903 is the verticaldistance from the centroid to the rear axle 119862119903 is the casterdamping 119871119891 is the vertical distance from the centroid to thefront axle 119868119911119911 is the moment of inertia of the vehicle bodyaround 119911-axis 119881119909 is the longitudinal speed
And 11988622 = minus(21198712
119891119862119891 + 2119871
2
119903119862119903)119868119911119911119881119909 where 119871119891 is the
vertical distance from the centroid to the front axle 119862119891 is thepitch damping 119871119903 is the vertical distance from the centroidto the rear axle 119862119903 is the caster damping 119868119911119911 is the momentof inertia of the vehicle body around 119911-axis and 119881119909 is thelongitudinal speed
33 ARS Subcontrol System Active four-wheel steering tech-nology can improve the handling stability of the vehicle at
high speed and the controlling flexibility at low speed Theideal yaw rate calculated by vehicle model of two degrees offreedom is the tracked target So ARS takes round steeringangle as the controlled variable
The system adopts sliding mode control strategy and thetracking error of yaw rate is defined as sliding mode variable
119904119904119903 = 119903 minus 119903idl (8)
where 119903 is the actual yaw rate and 119903idl is the ideal yaw rateThe condition for reaching the sliding surface is defined
as
119904119904119903 = minus120582119904119903119904119904119903 minus 120581119904119903 sat(119904119904119903
120576119904119903
) (9)
where
sat(119904119904119903
120576119904119903
) =
119904119904119903
120576119904119903
1003816100381610038161003816119904119904119903
1003816100381610038161003816lt 120576119904119903
sign(
119904119904119903
120576119904119903
) 1003816100381610038161003816119904119904119903
1003816100381610038161003816ge 120576119904119903
(10)
where 120582119904119903 and 120581119904119903 are all positive constants 120582119904119903 reflects theresponse speed of yaw tracking controller 120581119904119903 shows theconvergence rate of sliding mode surface of the system 119904119904119903is the tracking error of yaw rate and 120576119904119903 is the thickness ofboundary layer
In order to restrain the shake of high frequency causedby frequent switching on the sliding surface 120576119904119903 is taken asthe thickness of the boundary layer 120582119904119903 reflects the responsespeed of yaw tracking controller and 120581119904119903 reflects the rate howthe system reaches the sliding surface
34 Upper Supervisory Controller Design The control idea ofthe supervisory controller is as follows judging the steadystate of the vehicle according to the stability factor distribut-ing the weight of the control function of three subcontrollersand coordinating the output of each subcontroller
Firstly the stability factor of front and rear wheels isdefined as [17]
SF119891 =
100381610038161003816100381610038161198881120572119891 + 1198882119891
10038161003816100381610038161003816 (11)
where SF119891 is the possibility that the front wheels come intothe slipping state and 120572119891 is the corresponding sideslip angleof the middle of the left and right wheels on the front axle
SF119903 =10038161003816100381610038161198881120572119903 + 1198882119903
1003816100381610038161003816 (12)
where SF119903 is the possibility that the rear wheels come into theslipping state and 120572119903 is the corresponding sideslip angle of themiddle of the left and right wheels on the rear axle
1198881 and 1198882 can be obtained by analyzing the relationshipbetween the phase plane and the steering stability of the tire[18]
SF119891 and SF119903 show the possibility of the correspondingwheel beginning to side The larger the value the bigger theside slipping possibility of corresponding wheel namely thesmaller the control margin provided by the wheel Con-versely the smaller the value the greater the effective strengthof corresponding wheel
4 Journal of Sensors
Table 1 Rules of fuzzy controller of IVC
SF119891
SF119903
119882AFS 119882ARS 119882ESP
S S B B SS MS B M SS MB B S SS B B S MSMS S M B SMS MS M M MSMS MB M S MSMS B M S MBMB S M B SMB MS M M SMB MB M M MSMB B S S MBB S S B MSB MS S M MSB MB S S MBB B S S B
Through repeated simulation tests when SF119891 and SF119903 areless than 07 the active steering control of the front and rearwheels can meet the requirements of vehicle stability WhenSF119891 or SF119903 is bigger than 13 the use of ESP can be moreeffective to correct the excessive or lack steering state whichcan keep the vehicle stable fast When SF119891 and SF119903 are in therange from 07 to 13 the wheels with smaller stability factorprovide a greater role in vehicle stability control Based onthis the design of fuzzy logic controller is designed as follows
The controller takes the stability factors of the frontand rare wheels such as SF119891 and SF119903 as the input Themembership functions are in the same range [0 2] and thefuzzy subset is SMSMBB as shown in Figure 2(a) Theoutputs of the controller are the control weights of threesubcontrollers whose range is [0 1]
The membership functions of AFS and ARS are thesame and fuzzy subset is DME as shown in Figure 2(b)The membership function of ESP subcontroller is shown inFigure 2(c) and fuzzy subset is SMSMBBThe collectionof letters is as follows S is small M is medium and B is big
Considering the actual application of the computationand real-time all variables of the membership functions areeasy to be calculated by the procedure such as trigonometricfunction or trapezoidal function Table 1 shows the inferencerules of fuzzy controller of IVC
4 Network Topology Design of IVC-NCS
According to system control strategy of IVC combined withthe control requirements of vehicle stability the followingseveral points are considered as the basis for the designActual limitations of vehicle space layout are as followsbecause CAN network agreement and the correspondinginternational standards limit the length of the branchesconnecting the nodes and communication trunks so networknodes in the actual space layout is one of the major consid-erations of network topology structure Such as ARSC and
AFSC they are divided into two control units to control thesystem separately which is helpful to connect the sensors andthe executing agency
Load capacity constraint of network communication is asfollows for IVC-NCS if all sensors controllers and actuatorsexist as independent network nodes and the network worksin 250Kbps rate of regulated by vehicle high speed networkof SAE only from the theoretical calculation of CAN com-munication capability its load capacity is difficult to meetthe control requirements While the communication speedis increased to 500Kbps the anti-interference ability of thenode will be poor so it is difficult to realize the high speedcommunication in the bad electromagnetic environment
Real-time requirements of subsystems are as followsthree subsystems of IVC-NCS are the relatively indepen-dent closed-loop control system ESP subsystem has higherrequest on real-time of wheel speed signals which requiresthe executing agencies to react quickly according to controlorders
The sensors necessary for many systems are designed asindependent network nodes The subcontrol systems adopttraditional point-to-point connection in the controllers sen-sors and executing agencies Its object is to obtain satisfactoryreal-time performance and reliability
Based on above analysis the network in Figure 3 isdesigned as IVC-NCS structure CAN network is taken asthe communicationmedium of the controller node and eachsubsystem is connected with the traditional method of pointto point Considering that ESP system has obvious effect forvehicle stability in extreme conditions the supervision andcontrol tasks of the system and the control calculation of ESPare assigned to one node
The sensor signals are the basis of the controller tojudge the state of the vehicle and control instructions Whenthe network communication load suddenly increases theprobability of signal loss of low level sensors will be sig-nificantly increased Therefore in order to ensure the real-time performance of the sensor signal transmission themessage priority of the sensor nodes is set higher to avoidthe message loss in the control cycle which leads to controlinstability Table 2 shows the communication matrix table ofIVC-NCS Messages Msg7 and Msg9 as the state messagesof executing agencies can help the controller nodes tounderstand the operation status of the system Because theydo not participate in the control calculation so the priority islow and the transmission cycle is relatively large
5 Simulation and Result Analysis
According to nonlinear vehicle model with eight degreesof freedom to calculate the state of the vehicle Simulinkplatform is used for simulation Before the performance ofIVC-NCS the IVC system is simulated and tested to verifythe effectiveness of the controller
51 Effectiveness Verification of IVC System Control In orderto verify the effectiveness of IVC system the sine curve andthe step curve with the maximum value 5 degrees (about0087 rad) of the vehicle steering wheel are input to simulate
Journal of Sensors 5
Table 2 Communication matrix table of IVC-NCS
Message name Message content(signal) Transmission node Message property Signal description
Priority Cycle Data domainMsg1 Interf Node 1 1 Pending 8 bytes Meaningless messageMsg2 S Yaw Acc Node 2 2 119875 = 5ms 6 bytes Yaw rate and lateral accelerationMsg3 S StrWhAgl Node 3 3 119875 = 5ms 4 bytes Steering wheel angleMsg4 S Vx Node 4 4 119875 = 5ms 4 bytes Longitudinal speedMsg5 Weight Node 4 7 AP 4 bytes Control weight of AFS and ARSMsg6 S ARS Node 5 5 119875 = 5ms 8 bytes Speed and rotation angle of rear wheelMsg7 D ARS M Node 5 8 119875 = 20ms 4 bytes Rear wheel motor statusMsg8 S AFS Node 6 6 119875 = 5ms 8 bytes Speed and rotation angle of front wheelMsg9 D AFS M Node 6 9 119875 = 20ms 4 bytes Front wheel motor status
S B
00
1MS MB
05
06 07 08 09 10 11 12 13 14 2
(a)
M BS
0
1
05
0 01 02 03 04 05 06 07 08 09 1
(b)
MS BS
0
1 MB
05
0 01 02 03 04 05 06 07 08 09 1
(c)
Figure 2 (a) SF119891 and SF119903 (b) Control weight 119882AFS 119882ARS of AFS and ARS (c) Control weight 119882ESP of ESP
Combinationsensor node
Node 2
Nonlinear vehicle model with eightdegrees of freedom
Rear wheelangle sensor
Rear wheelspeed sensor speed sensor
Vehicle
Rear wheelsteering drive
Rear wheelcontroller node(ARS) Node 5
Brake valve
Supervisorycontroller node(ESP) Node 4
Front wheelcontroller node(AFS) Node 6
speed sensorFront wheel Rear wheel
steering drive
CAN
sensor nodeSteering wheel angle
Node 3
Front wheelangle sensor
Interfering nodeNode 1
Figure 3 IVC-NCS network structure
6 Journal of Sensors
0 2 4 6minus01
0
01
Time (s) Time (s)0 02 04 06 08 1
0
005
01
Ang
le (r
ad)
Ang
le (r
ad)
(a)
0 1 2 3 4 5 6minus04
minus02
0
02
04
Yaw
rate
(rad
s)
Time (s)
Ideal value
No controlIn control
(b)
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
Yaw
rate
(rad
s)
Time (s)
Ideal value
No controlIn control
(c)
Figure 4 (a) Input curve of front wheel angle (b)The response curve of yaw rate of the steering wheel with sine angle input (c)The responsecurve of yaw rate of the steering wheel with step angle input
the tracking response of the vehicle under different inputyaw rates According to the transmission ratio of the steeringsystem the corresponding input curve of front wheel steeringangle is shown in Figure 4(a) The vehicle travels at a goodroad with a adhesion coefficient of 085 and the initial speedis 25ms
Figures 4(b) and 4(c) are the response curves of vehicleyaw rate at different angle inputs It can be seen that the yawrate of the controlled vehicle can quickly and effectively trackthe ideal value when compared with the system without thecontrol For the sine input the execution of the vehicle is anonstandard single lane change test At this time due to thecorrection function of angle changes of the front wheel soafter the apparent slip the yaw rate is settled in zero value asshown in Figure 4(b)
Under the step input of steering wheel angle in Fig-ure 4(c) the yaw rate of the vehicle without control cannottrack the ideal value which appears as the trend of diver-gence So the vehicle cannot achieve stable circular motionand rollover because of instabilityThe yaw rate of the vehiclewith controllers is good at tracking the ideal value Simulationresults show that the IVC system can effectively improvethe stability vehicle in critical conditions which verifies theeffectiveness of the designed control system
52 Simulation Analysis of IVC-NCS Based on CAN In orderto investigate the performance change of the designed IVCsystem after the CAN network is involved in the control
the stability of the vehicle was investigated using the samestep input of the steering wheel The initial speed is 25msand the road adhesion coefficient is 085 Considering thepractical application of CAN network with high speed thecommunication rate is set to 250Kbps Node sends only dataframes If the interfering nodes do not send any messagethe network load is about 84 when the maximum is filledWhen the interference nodes send the interference messageof high priority with 4ms cycle it can ensure that the networkload is close to 1 but less than the network bandwidth whichensures the system communication not to lose the frames
According to the assumptions and simulation conditionsFigure 5 shows the comparison curve of yaw rate trackingaboutCANnetwork communication and point-to-point con-nection Compared with point-to-point connection modethe IVC system with CAN network connection can quicklyand effectively track the ideal value under the conditionof good network environment without changing the steadystate of the control system It can be clearly seen that inthe part of the amplified image the network involves inthe control system which makes the yaw rate fluctuate withmicroamplitude The overshoot of control increases from31 of the point-to-point connection to 6 of the CANnetwork connection
In order to investigate the influence of different networkstate on the control performance of the system the trackingsimulation test of vehicle yaw rate is carried out for differentnetwork load and packet loss rate
Journal of Sensors 7
0 01 02 03 04 05 06 07 08 09 10
00501
01502
02503
03504
035 04 045 05 055032034036038
Yaw
rate
(rad
s)
Time (s)
Ideal value
CANPoint to point
Figure 5 The response curve of IVC yaw rate of CAN networkconnection
Figure 6 shows the response curve of different packet lossrates of IVC-NCS yaw rate In the simulation process theinterference nodes do not send the messages It can be seenthat when the packet loss rate is lower than 20 the dynamiccharacteristic of the system becomes bad In the packet lossrate of 5 and 20 the corresponding overshoots of thesystem are about 9 and 125 In 03 s after the step input offrontwheel ends the vehicle yaw rate can be stable to track theideal valueWhen the packet loss rate is less than 40 the yawrate of the vehicle can be finally stabilized at an ideal valueWhen the packet loss rate is more than 40 the yaw rate isobviously fluctuated in the ideal yaw rate tracking process At50 the overshoot of yaw rate increases rapidly to about 42the vehicle begins to sideslip
When the packet loss rate is up to 60 the vehicleyaw rate tracking is seriously lagging behind which cannotachieve stable circular motion The analysis shows that whenthe packet loss rate is low themessage transmission keep highsuccess rate The information of the sensors can be obtainedby control nodes in time so the controller works fast withlittle effect on the performance of system control With theincrease of packet loss rate the control instructions cannotbe timely generated and executed which makes the controlcycle become longer The status of executing agency cannotbe corrected in time The input of executing agency will betoo large or too small which causes the control to fail
Figure 7 shows that the interference nodes send themessages of highest priority in 4ms cycle and the networkload is close to 1 The long dashes are the response curve ofyaw rate of CAN network without the interference when thenetwork load is about 84 The short dashed lines dashed-dotted lines and bold dashed lines are separately responsecurves of yaw rate at 119905 different packet loss rates when theload is full
Under the condition tomeet the communication require-ments of the control system when the network load isclose to 1 the induced delay of the system is largest It canbe calculated when the network load increases from 84to nearly 100 and the overshoot increases from 6 to 7When the network load is 1 and packet loss rate is 30
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
Yaw
rate
(rad
s)
Time (s)
Ideal valuePoint to pointLoss 5Loss 20
Loss 40Loss 50Loss 60
Figure 6 The response curve of different packet loss rates
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
04 045 05 055 06034
036
038
04
Yaw
rate
(rad
s)
Time (s)
Ideal valuePoint to pointNo interference
No lossLoss 30Loss 50
Figure 7 The response curve of yaw rate with full load of networkcommunication
the overshoot of yaw rate is 157 Therefore although thenetwork load increases as long as network load can meetthe communication requirements of the control system thenetwork intervention only has little effect on the qualityof dynamic control which does not change the steadycharacteristics of the system The vehicle can achieve thestable circular motion within 03 s of the yaw rare input ofthe front wheel
When the communication network is fully loaded andthe packet loss rate is 50 the vehicle cannot completethe scheduled circular motion The yaw rate of the vehiclediverges to make the vehicle out of control The simulationresults show that when the network bandwidth meets theneeds of control system the effect of the network induceddelay of control system is very small and negligible And thenetwork packet loss will affect the performance of controlsystem seriously When the packet loss rate is up to 50 thesystem control performance will deteriorate significantly
8 Journal of Sensors
53 Stability and Coordination Analysis From the devel-opment of the vehicle chassis control system the trend ofintegration and network is very obvious The system controlarchitecture and the network architecture form have differenteffects on the stability control of the chassis In this paperthe design of the control system fully takes into account thestability of the chassis control performance
Because ABS is the basis for the realization of ESPand the latter needs to achieve the independent control ofbraking intensity about the four wheels so ABS is designedas an independent four-channel mode As one kind of thecontroller associated with safety and real-time the executionand controller of ABS usually adopt directly connectedmanner in order to reduce the information switching delayand ensure the safety and stability of the vehicle
The control target of ESP system is to control the stabilityof the vehicle in the extreme conditions through the controlof braking strength of four wheels to achieve the activesafety In order to improve the unstable state of the vehiclein extreme conditions applying the braking force on inwardrear wheel with the understeer or on the outward front wheelwith the oversteer can quickly and effectively improve thestability Taking into account that ESP system has the obviouseffect on the vehicle stability in extreme conditions thestudy will assign the supervision and control tasks and thecalculation of ESP control to one node
For the performance of network control system commu-nication real-time performance is the most important factoraffecting the control performance which can be expressedand measured by network delay The existence of networkdelay reduces the control performance of the system whichwill lead to the loss of stability of the stable control system
Especially in extreme conditions the change of thevehicle state is largerWhen a large number of control instruc-tions are lost the adjustment of the new and old controlinstructions is bound to increase because of the large numberof cycles which will increase the action range of the actuatorTherefore toomuch data packet loss is extremely unfavorablefor the stability control When the packet loss rate is lessthan a certain value only the system dynamic characteristicbecomesworse and the system stability is not changedWhenthe packet loss rate reaches the critical value the systemcontrol stability is close to the critical state
In addition through the simulation experiment we canknow that CAN network intervention did not significantlyaffect the stability of vehicle braking Therefore when CANnetwork communication environment is good the networkinduced delay of CAN network has a little influence on theperformance of the controller which indicates that the ABScontroller built in this research has strong robustness on asingle road
In the 80s of last century the researchers began to tryto decompose the complex chassis control problem into anumber of subcontrol systems and then use a mechanism tocoordinate the dynamic relationship between the subsys-tems to meet the control requirements Using the uppercoordinated control for the integrated control architectureof multiple independent control units of the vehicle chassiscan effectively adjust the collaborative work of the control
units avoid the conflict between the controllers and makethe vehicle obtain optimal running state
The supervision mechanism is based on a hierarchicalcontrol principle combinedwith fuzzy control logic to designa controller to supervise and coordinate ESP AFS and ARSThe target of the upper supervisory controller according tothe stability factor to judge vehicle steady state is to redistrib-ute the control weights of three subsystems and coordinatethe output of each subcontroller
The sensors necessary for many systems are designed asindependent network nodes The subcontrol systems adopttraditional point-to-point connection in the controllers sen-sors and executing agencies Its object is to obtain satisfactoryreal-time performance and coordination
6 Conclusions
In this paper the vehicle chassis control system is taken as theapplication of CAN network The target focuses on how thenetwork affects the control system The ABS ASC and IVCare simulated The main research contents and conclusionsare as follows
According to the control theory of sliding mode ESP andAFS subcontrollers are designed to track the ideal yaw rateBased on the principle of hierarchical control and fuzzy con-trol a fuzzy controller is designed which is used to monitorand coordinate the ESP AFS andARS And the IVC system isconstructed with the upper supervisory controller and threesubcontrol systems on the Simulink platform Comparedwith the point-to-point connection the system simulationof IVC-NCS shows that the control of the integrated controlsystem has good performance
According to the IVC based on the supervision mech-anism combined with the function of each subsystem thenetwork topology structure of IVC is proposed and theIVC communication matrix based on CAN network com-munication is designed With the common sensors andthe subcontrollers as the CAN network independent nodesthe network induced delay and packet loss rate on thesystem control performance are studied by simulation Thesimulation results show that the network does not lose frameand even if the network traffic load is close to 1 the networkintervention of IVC can only show the very small change ofthe dynamic quality of the system The network packet losshas a significant impact on the performance of the systemcontrol When the packet loss rate is less than 30 only thesystem dynamic performance becomes worse and the systemstability does not change When the packet loss rate is up to50 the system control stability is close to the critical stateand the vehicle is unstable
Competing Interests
The authors declare that they have no competing interests
Acknowledgments
This study was funded byThe Natural Science Foundation ofJiangsu Province (BK20130977)
Journal of Sensors 9
References
[1] T Gordon M Howell and F Brandao ldquoIntegrated controlmethodologies for road vehiclesrdquoVehicle System Dynamics vol40 no 1ndash3 pp 157ndash190 2003
[2] H Chou and B DrsquoAndrea-Novel ldquoGlobal vehicle control usingdifferential braking torques and active suspension forcesrdquoVehicle System Dynamics vol 43 no 4 pp 261ndash284 2005
[3] C B Chu and W W Chen ldquoVehicle chassis system based onlayered coordinated controlrdquo Chinese Journal of MechanicalEngineering vol 44 no 2 pp 157ndash162 2008
[4] H Zhu and W W Chen ldquoActive control of vehicle suspensionand steering system based on strategy hierarchyrdquo Chinese Jour-nal of Agricultural Machinery vol 39 no 10 pp 1ndash6 2008
[5] M J L Boada B L Boada A Munoz and V Diaz ldquoIntegratedcontrol of front-wheel steering and front braking forces on thebasis of fuzzy logicrdquo Proceedings of the Institution of MechanicalEngineers Part D Journal of Automobile Engineering vol 220no 3 pp 253ndash267 2006
[6] D Li X Shen and F Yu ldquoIntegrated vehicle chassis control witha mainservo-loop structurerdquo International Journal of Automo-tive Technology vol 7 no 7 pp 803ndash812 2006
[7] E J Bedner and H H Chen ldquoA supervisory control to managebrakes and four-wheel-steer systemsrdquo SAE Paper 2004-01-10592004
[8] S Chang and T J Gordon ldquoA flexible hierarchical model-basedcontrol methodology for vehicle active safety systemsrdquo VehicleSystem Dynamics vol 46 supplement 1 pp 63ndash75 2008
[9] N Kelling ldquoThe BRAKE projectmdashcentralized versus dis-tributed redundancy for brake-by-wire systemsrdquo SAE Paper2002-01-0266 SAE International 2002
[10] J X Wang Research of Integrated Control System of VehicleChassis Based on Multi Agent Southeast University NanjingChina 2010
[11] Y Zhang C L Yin and J W Zhang ldquoA real time estimationmethod for the lateral velocity of the center of mass of thevehiclerdquo Chinese Journal of Mechanical Engineering vol 44 no2 pp 219ndash222 2008
[12] D Crolla and Y Fan Vehicle Dynamics and Control ChineseCommunications Press Beijing China 2003
[13] H B Pacejka andE Bakker ldquoMagic formula tyremodelrdquoVehicleSystem Dynamics vol 21 no 1 pp 1ndash18 1993
[14] H B Pacejka and I J M Besselink ldquoMagic formula tyre modelwith transient propertiesrdquo Vehicle System Dynamics vol 27supplement 1 pp 234ndash249 1997
[15] K H Guo and L Ren ldquoA unifield semi-empirical tire modelwith higher accuracy and less parametersrdquo SAETechnical PaperSeries 1999-01-0785 SAE International 1999
[16] H Dugoff P S Fancher and L Segal ldquoTyre performance char-acteristics affecting vehicle response to steering and brakingcontrol inputsrdquo Final Report US National 1969
[17] W Jinxiang and C Nan ldquoResearch on supervisory controlbased integrated chassis control framework and its simulationrdquoTransactions of the Chinese Society of Agricultural Machineryvol 40 no 9 pp 1ndash6 2009
[18] S Inagaki I Kushiro and M Yamamoto ldquoAnalysis on vehiclestability in critical cornering using phase-plane methodrdquo inProceedings of the International Symposium on Advanced VehicleControl (AVEC rsquo94) pp 287ndash292 Tsukuba- Shi Japan 1994
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
4 Journal of Sensors
Table 1 Rules of fuzzy controller of IVC
SF119891
SF119903
119882AFS 119882ARS 119882ESP
S S B B SS MS B M SS MB B S SS B B S MSMS S M B SMS MS M M MSMS MB M S MSMS B M S MBMB S M B SMB MS M M SMB MB M M MSMB B S S MBB S S B MSB MS S M MSB MB S S MBB B S S B
Through repeated simulation tests when SF119891 and SF119903 areless than 07 the active steering control of the front and rearwheels can meet the requirements of vehicle stability WhenSF119891 or SF119903 is bigger than 13 the use of ESP can be moreeffective to correct the excessive or lack steering state whichcan keep the vehicle stable fast When SF119891 and SF119903 are in therange from 07 to 13 the wheels with smaller stability factorprovide a greater role in vehicle stability control Based onthis the design of fuzzy logic controller is designed as follows
The controller takes the stability factors of the frontand rare wheels such as SF119891 and SF119903 as the input Themembership functions are in the same range [0 2] and thefuzzy subset is SMSMBB as shown in Figure 2(a) Theoutputs of the controller are the control weights of threesubcontrollers whose range is [0 1]
The membership functions of AFS and ARS are thesame and fuzzy subset is DME as shown in Figure 2(b)The membership function of ESP subcontroller is shown inFigure 2(c) and fuzzy subset is SMSMBBThe collectionof letters is as follows S is small M is medium and B is big
Considering the actual application of the computationand real-time all variables of the membership functions areeasy to be calculated by the procedure such as trigonometricfunction or trapezoidal function Table 1 shows the inferencerules of fuzzy controller of IVC
4 Network Topology Design of IVC-NCS
According to system control strategy of IVC combined withthe control requirements of vehicle stability the followingseveral points are considered as the basis for the designActual limitations of vehicle space layout are as followsbecause CAN network agreement and the correspondinginternational standards limit the length of the branchesconnecting the nodes and communication trunks so networknodes in the actual space layout is one of the major consid-erations of network topology structure Such as ARSC and
AFSC they are divided into two control units to control thesystem separately which is helpful to connect the sensors andthe executing agency
Load capacity constraint of network communication is asfollows for IVC-NCS if all sensors controllers and actuatorsexist as independent network nodes and the network worksin 250Kbps rate of regulated by vehicle high speed networkof SAE only from the theoretical calculation of CAN com-munication capability its load capacity is difficult to meetthe control requirements While the communication speedis increased to 500Kbps the anti-interference ability of thenode will be poor so it is difficult to realize the high speedcommunication in the bad electromagnetic environment
Real-time requirements of subsystems are as followsthree subsystems of IVC-NCS are the relatively indepen-dent closed-loop control system ESP subsystem has higherrequest on real-time of wheel speed signals which requiresthe executing agencies to react quickly according to controlorders
The sensors necessary for many systems are designed asindependent network nodes The subcontrol systems adopttraditional point-to-point connection in the controllers sen-sors and executing agencies Its object is to obtain satisfactoryreal-time performance and reliability
Based on above analysis the network in Figure 3 isdesigned as IVC-NCS structure CAN network is taken asthe communicationmedium of the controller node and eachsubsystem is connected with the traditional method of pointto point Considering that ESP system has obvious effect forvehicle stability in extreme conditions the supervision andcontrol tasks of the system and the control calculation of ESPare assigned to one node
The sensor signals are the basis of the controller tojudge the state of the vehicle and control instructions Whenthe network communication load suddenly increases theprobability of signal loss of low level sensors will be sig-nificantly increased Therefore in order to ensure the real-time performance of the sensor signal transmission themessage priority of the sensor nodes is set higher to avoidthe message loss in the control cycle which leads to controlinstability Table 2 shows the communication matrix table ofIVC-NCS Messages Msg7 and Msg9 as the state messagesof executing agencies can help the controller nodes tounderstand the operation status of the system Because theydo not participate in the control calculation so the priority islow and the transmission cycle is relatively large
5 Simulation and Result Analysis
According to nonlinear vehicle model with eight degreesof freedom to calculate the state of the vehicle Simulinkplatform is used for simulation Before the performance ofIVC-NCS the IVC system is simulated and tested to verifythe effectiveness of the controller
51 Effectiveness Verification of IVC System Control In orderto verify the effectiveness of IVC system the sine curve andthe step curve with the maximum value 5 degrees (about0087 rad) of the vehicle steering wheel are input to simulate
Journal of Sensors 5
Table 2 Communication matrix table of IVC-NCS
Message name Message content(signal) Transmission node Message property Signal description
Priority Cycle Data domainMsg1 Interf Node 1 1 Pending 8 bytes Meaningless messageMsg2 S Yaw Acc Node 2 2 119875 = 5ms 6 bytes Yaw rate and lateral accelerationMsg3 S StrWhAgl Node 3 3 119875 = 5ms 4 bytes Steering wheel angleMsg4 S Vx Node 4 4 119875 = 5ms 4 bytes Longitudinal speedMsg5 Weight Node 4 7 AP 4 bytes Control weight of AFS and ARSMsg6 S ARS Node 5 5 119875 = 5ms 8 bytes Speed and rotation angle of rear wheelMsg7 D ARS M Node 5 8 119875 = 20ms 4 bytes Rear wheel motor statusMsg8 S AFS Node 6 6 119875 = 5ms 8 bytes Speed and rotation angle of front wheelMsg9 D AFS M Node 6 9 119875 = 20ms 4 bytes Front wheel motor status
S B
00
1MS MB
05
06 07 08 09 10 11 12 13 14 2
(a)
M BS
0
1
05
0 01 02 03 04 05 06 07 08 09 1
(b)
MS BS
0
1 MB
05
0 01 02 03 04 05 06 07 08 09 1
(c)
Figure 2 (a) SF119891 and SF119903 (b) Control weight 119882AFS 119882ARS of AFS and ARS (c) Control weight 119882ESP of ESP
Combinationsensor node
Node 2
Nonlinear vehicle model with eightdegrees of freedom
Rear wheelangle sensor
Rear wheelspeed sensor speed sensor
Vehicle
Rear wheelsteering drive
Rear wheelcontroller node(ARS) Node 5
Brake valve
Supervisorycontroller node(ESP) Node 4
Front wheelcontroller node(AFS) Node 6
speed sensorFront wheel Rear wheel
steering drive
CAN
sensor nodeSteering wheel angle
Node 3
Front wheelangle sensor
Interfering nodeNode 1
Figure 3 IVC-NCS network structure
6 Journal of Sensors
0 2 4 6minus01
0
01
Time (s) Time (s)0 02 04 06 08 1
0
005
01
Ang
le (r
ad)
Ang
le (r
ad)
(a)
0 1 2 3 4 5 6minus04
minus02
0
02
04
Yaw
rate
(rad
s)
Time (s)
Ideal value
No controlIn control
(b)
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
Yaw
rate
(rad
s)
Time (s)
Ideal value
No controlIn control
(c)
Figure 4 (a) Input curve of front wheel angle (b)The response curve of yaw rate of the steering wheel with sine angle input (c)The responsecurve of yaw rate of the steering wheel with step angle input
the tracking response of the vehicle under different inputyaw rates According to the transmission ratio of the steeringsystem the corresponding input curve of front wheel steeringangle is shown in Figure 4(a) The vehicle travels at a goodroad with a adhesion coefficient of 085 and the initial speedis 25ms
Figures 4(b) and 4(c) are the response curves of vehicleyaw rate at different angle inputs It can be seen that the yawrate of the controlled vehicle can quickly and effectively trackthe ideal value when compared with the system without thecontrol For the sine input the execution of the vehicle is anonstandard single lane change test At this time due to thecorrection function of angle changes of the front wheel soafter the apparent slip the yaw rate is settled in zero value asshown in Figure 4(b)
Under the step input of steering wheel angle in Fig-ure 4(c) the yaw rate of the vehicle without control cannottrack the ideal value which appears as the trend of diver-gence So the vehicle cannot achieve stable circular motionand rollover because of instabilityThe yaw rate of the vehiclewith controllers is good at tracking the ideal value Simulationresults show that the IVC system can effectively improvethe stability vehicle in critical conditions which verifies theeffectiveness of the designed control system
52 Simulation Analysis of IVC-NCS Based on CAN In orderto investigate the performance change of the designed IVCsystem after the CAN network is involved in the control
the stability of the vehicle was investigated using the samestep input of the steering wheel The initial speed is 25msand the road adhesion coefficient is 085 Considering thepractical application of CAN network with high speed thecommunication rate is set to 250Kbps Node sends only dataframes If the interfering nodes do not send any messagethe network load is about 84 when the maximum is filledWhen the interference nodes send the interference messageof high priority with 4ms cycle it can ensure that the networkload is close to 1 but less than the network bandwidth whichensures the system communication not to lose the frames
According to the assumptions and simulation conditionsFigure 5 shows the comparison curve of yaw rate trackingaboutCANnetwork communication and point-to-point con-nection Compared with point-to-point connection modethe IVC system with CAN network connection can quicklyand effectively track the ideal value under the conditionof good network environment without changing the steadystate of the control system It can be clearly seen that inthe part of the amplified image the network involves inthe control system which makes the yaw rate fluctuate withmicroamplitude The overshoot of control increases from31 of the point-to-point connection to 6 of the CANnetwork connection
In order to investigate the influence of different networkstate on the control performance of the system the trackingsimulation test of vehicle yaw rate is carried out for differentnetwork load and packet loss rate
Journal of Sensors 7
0 01 02 03 04 05 06 07 08 09 10
00501
01502
02503
03504
035 04 045 05 055032034036038
Yaw
rate
(rad
s)
Time (s)
Ideal value
CANPoint to point
Figure 5 The response curve of IVC yaw rate of CAN networkconnection
Figure 6 shows the response curve of different packet lossrates of IVC-NCS yaw rate In the simulation process theinterference nodes do not send the messages It can be seenthat when the packet loss rate is lower than 20 the dynamiccharacteristic of the system becomes bad In the packet lossrate of 5 and 20 the corresponding overshoots of thesystem are about 9 and 125 In 03 s after the step input offrontwheel ends the vehicle yaw rate can be stable to track theideal valueWhen the packet loss rate is less than 40 the yawrate of the vehicle can be finally stabilized at an ideal valueWhen the packet loss rate is more than 40 the yaw rate isobviously fluctuated in the ideal yaw rate tracking process At50 the overshoot of yaw rate increases rapidly to about 42the vehicle begins to sideslip
When the packet loss rate is up to 60 the vehicleyaw rate tracking is seriously lagging behind which cannotachieve stable circular motion The analysis shows that whenthe packet loss rate is low themessage transmission keep highsuccess rate The information of the sensors can be obtainedby control nodes in time so the controller works fast withlittle effect on the performance of system control With theincrease of packet loss rate the control instructions cannotbe timely generated and executed which makes the controlcycle become longer The status of executing agency cannotbe corrected in time The input of executing agency will betoo large or too small which causes the control to fail
Figure 7 shows that the interference nodes send themessages of highest priority in 4ms cycle and the networkload is close to 1 The long dashes are the response curve ofyaw rate of CAN network without the interference when thenetwork load is about 84 The short dashed lines dashed-dotted lines and bold dashed lines are separately responsecurves of yaw rate at 119905 different packet loss rates when theload is full
Under the condition tomeet the communication require-ments of the control system when the network load isclose to 1 the induced delay of the system is largest It canbe calculated when the network load increases from 84to nearly 100 and the overshoot increases from 6 to 7When the network load is 1 and packet loss rate is 30
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
Yaw
rate
(rad
s)
Time (s)
Ideal valuePoint to pointLoss 5Loss 20
Loss 40Loss 50Loss 60
Figure 6 The response curve of different packet loss rates
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
04 045 05 055 06034
036
038
04
Yaw
rate
(rad
s)
Time (s)
Ideal valuePoint to pointNo interference
No lossLoss 30Loss 50
Figure 7 The response curve of yaw rate with full load of networkcommunication
the overshoot of yaw rate is 157 Therefore although thenetwork load increases as long as network load can meetthe communication requirements of the control system thenetwork intervention only has little effect on the qualityof dynamic control which does not change the steadycharacteristics of the system The vehicle can achieve thestable circular motion within 03 s of the yaw rare input ofthe front wheel
When the communication network is fully loaded andthe packet loss rate is 50 the vehicle cannot completethe scheduled circular motion The yaw rate of the vehiclediverges to make the vehicle out of control The simulationresults show that when the network bandwidth meets theneeds of control system the effect of the network induceddelay of control system is very small and negligible And thenetwork packet loss will affect the performance of controlsystem seriously When the packet loss rate is up to 50 thesystem control performance will deteriorate significantly
8 Journal of Sensors
53 Stability and Coordination Analysis From the devel-opment of the vehicle chassis control system the trend ofintegration and network is very obvious The system controlarchitecture and the network architecture form have differenteffects on the stability control of the chassis In this paperthe design of the control system fully takes into account thestability of the chassis control performance
Because ABS is the basis for the realization of ESPand the latter needs to achieve the independent control ofbraking intensity about the four wheels so ABS is designedas an independent four-channel mode As one kind of thecontroller associated with safety and real-time the executionand controller of ABS usually adopt directly connectedmanner in order to reduce the information switching delayand ensure the safety and stability of the vehicle
The control target of ESP system is to control the stabilityof the vehicle in the extreme conditions through the controlof braking strength of four wheels to achieve the activesafety In order to improve the unstable state of the vehiclein extreme conditions applying the braking force on inwardrear wheel with the understeer or on the outward front wheelwith the oversteer can quickly and effectively improve thestability Taking into account that ESP system has the obviouseffect on the vehicle stability in extreme conditions thestudy will assign the supervision and control tasks and thecalculation of ESP control to one node
For the performance of network control system commu-nication real-time performance is the most important factoraffecting the control performance which can be expressedand measured by network delay The existence of networkdelay reduces the control performance of the system whichwill lead to the loss of stability of the stable control system
Especially in extreme conditions the change of thevehicle state is largerWhen a large number of control instruc-tions are lost the adjustment of the new and old controlinstructions is bound to increase because of the large numberof cycles which will increase the action range of the actuatorTherefore toomuch data packet loss is extremely unfavorablefor the stability control When the packet loss rate is lessthan a certain value only the system dynamic characteristicbecomesworse and the system stability is not changedWhenthe packet loss rate reaches the critical value the systemcontrol stability is close to the critical state
In addition through the simulation experiment we canknow that CAN network intervention did not significantlyaffect the stability of vehicle braking Therefore when CANnetwork communication environment is good the networkinduced delay of CAN network has a little influence on theperformance of the controller which indicates that the ABScontroller built in this research has strong robustness on asingle road
In the 80s of last century the researchers began to tryto decompose the complex chassis control problem into anumber of subcontrol systems and then use a mechanism tocoordinate the dynamic relationship between the subsys-tems to meet the control requirements Using the uppercoordinated control for the integrated control architectureof multiple independent control units of the vehicle chassiscan effectively adjust the collaborative work of the control
units avoid the conflict between the controllers and makethe vehicle obtain optimal running state
The supervision mechanism is based on a hierarchicalcontrol principle combinedwith fuzzy control logic to designa controller to supervise and coordinate ESP AFS and ARSThe target of the upper supervisory controller according tothe stability factor to judge vehicle steady state is to redistrib-ute the control weights of three subsystems and coordinatethe output of each subcontroller
The sensors necessary for many systems are designed asindependent network nodes The subcontrol systems adopttraditional point-to-point connection in the controllers sen-sors and executing agencies Its object is to obtain satisfactoryreal-time performance and coordination
6 Conclusions
In this paper the vehicle chassis control system is taken as theapplication of CAN network The target focuses on how thenetwork affects the control system The ABS ASC and IVCare simulated The main research contents and conclusionsare as follows
According to the control theory of sliding mode ESP andAFS subcontrollers are designed to track the ideal yaw rateBased on the principle of hierarchical control and fuzzy con-trol a fuzzy controller is designed which is used to monitorand coordinate the ESP AFS andARS And the IVC system isconstructed with the upper supervisory controller and threesubcontrol systems on the Simulink platform Comparedwith the point-to-point connection the system simulationof IVC-NCS shows that the control of the integrated controlsystem has good performance
According to the IVC based on the supervision mech-anism combined with the function of each subsystem thenetwork topology structure of IVC is proposed and theIVC communication matrix based on CAN network com-munication is designed With the common sensors andthe subcontrollers as the CAN network independent nodesthe network induced delay and packet loss rate on thesystem control performance are studied by simulation Thesimulation results show that the network does not lose frameand even if the network traffic load is close to 1 the networkintervention of IVC can only show the very small change ofthe dynamic quality of the system The network packet losshas a significant impact on the performance of the systemcontrol When the packet loss rate is less than 30 only thesystem dynamic performance becomes worse and the systemstability does not change When the packet loss rate is up to50 the system control stability is close to the critical stateand the vehicle is unstable
Competing Interests
The authors declare that they have no competing interests
Acknowledgments
This study was funded byThe Natural Science Foundation ofJiangsu Province (BK20130977)
Journal of Sensors 9
References
[1] T Gordon M Howell and F Brandao ldquoIntegrated controlmethodologies for road vehiclesrdquoVehicle System Dynamics vol40 no 1ndash3 pp 157ndash190 2003
[2] H Chou and B DrsquoAndrea-Novel ldquoGlobal vehicle control usingdifferential braking torques and active suspension forcesrdquoVehicle System Dynamics vol 43 no 4 pp 261ndash284 2005
[3] C B Chu and W W Chen ldquoVehicle chassis system based onlayered coordinated controlrdquo Chinese Journal of MechanicalEngineering vol 44 no 2 pp 157ndash162 2008
[4] H Zhu and W W Chen ldquoActive control of vehicle suspensionand steering system based on strategy hierarchyrdquo Chinese Jour-nal of Agricultural Machinery vol 39 no 10 pp 1ndash6 2008
[5] M J L Boada B L Boada A Munoz and V Diaz ldquoIntegratedcontrol of front-wheel steering and front braking forces on thebasis of fuzzy logicrdquo Proceedings of the Institution of MechanicalEngineers Part D Journal of Automobile Engineering vol 220no 3 pp 253ndash267 2006
[6] D Li X Shen and F Yu ldquoIntegrated vehicle chassis control witha mainservo-loop structurerdquo International Journal of Automo-tive Technology vol 7 no 7 pp 803ndash812 2006
[7] E J Bedner and H H Chen ldquoA supervisory control to managebrakes and four-wheel-steer systemsrdquo SAE Paper 2004-01-10592004
[8] S Chang and T J Gordon ldquoA flexible hierarchical model-basedcontrol methodology for vehicle active safety systemsrdquo VehicleSystem Dynamics vol 46 supplement 1 pp 63ndash75 2008
[9] N Kelling ldquoThe BRAKE projectmdashcentralized versus dis-tributed redundancy for brake-by-wire systemsrdquo SAE Paper2002-01-0266 SAE International 2002
[10] J X Wang Research of Integrated Control System of VehicleChassis Based on Multi Agent Southeast University NanjingChina 2010
[11] Y Zhang C L Yin and J W Zhang ldquoA real time estimationmethod for the lateral velocity of the center of mass of thevehiclerdquo Chinese Journal of Mechanical Engineering vol 44 no2 pp 219ndash222 2008
[12] D Crolla and Y Fan Vehicle Dynamics and Control ChineseCommunications Press Beijing China 2003
[13] H B Pacejka andE Bakker ldquoMagic formula tyremodelrdquoVehicleSystem Dynamics vol 21 no 1 pp 1ndash18 1993
[14] H B Pacejka and I J M Besselink ldquoMagic formula tyre modelwith transient propertiesrdquo Vehicle System Dynamics vol 27supplement 1 pp 234ndash249 1997
[15] K H Guo and L Ren ldquoA unifield semi-empirical tire modelwith higher accuracy and less parametersrdquo SAETechnical PaperSeries 1999-01-0785 SAE International 1999
[16] H Dugoff P S Fancher and L Segal ldquoTyre performance char-acteristics affecting vehicle response to steering and brakingcontrol inputsrdquo Final Report US National 1969
[17] W Jinxiang and C Nan ldquoResearch on supervisory controlbased integrated chassis control framework and its simulationrdquoTransactions of the Chinese Society of Agricultural Machineryvol 40 no 9 pp 1ndash6 2009
[18] S Inagaki I Kushiro and M Yamamoto ldquoAnalysis on vehiclestability in critical cornering using phase-plane methodrdquo inProceedings of the International Symposium on Advanced VehicleControl (AVEC rsquo94) pp 287ndash292 Tsukuba- Shi Japan 1994
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
Journal of Sensors 5
Table 2 Communication matrix table of IVC-NCS
Message name Message content(signal) Transmission node Message property Signal description
Priority Cycle Data domainMsg1 Interf Node 1 1 Pending 8 bytes Meaningless messageMsg2 S Yaw Acc Node 2 2 119875 = 5ms 6 bytes Yaw rate and lateral accelerationMsg3 S StrWhAgl Node 3 3 119875 = 5ms 4 bytes Steering wheel angleMsg4 S Vx Node 4 4 119875 = 5ms 4 bytes Longitudinal speedMsg5 Weight Node 4 7 AP 4 bytes Control weight of AFS and ARSMsg6 S ARS Node 5 5 119875 = 5ms 8 bytes Speed and rotation angle of rear wheelMsg7 D ARS M Node 5 8 119875 = 20ms 4 bytes Rear wheel motor statusMsg8 S AFS Node 6 6 119875 = 5ms 8 bytes Speed and rotation angle of front wheelMsg9 D AFS M Node 6 9 119875 = 20ms 4 bytes Front wheel motor status
S B
00
1MS MB
05
06 07 08 09 10 11 12 13 14 2
(a)
M BS
0
1
05
0 01 02 03 04 05 06 07 08 09 1
(b)
MS BS
0
1 MB
05
0 01 02 03 04 05 06 07 08 09 1
(c)
Figure 2 (a) SF119891 and SF119903 (b) Control weight 119882AFS 119882ARS of AFS and ARS (c) Control weight 119882ESP of ESP
Combinationsensor node
Node 2
Nonlinear vehicle model with eightdegrees of freedom
Rear wheelangle sensor
Rear wheelspeed sensor speed sensor
Vehicle
Rear wheelsteering drive
Rear wheelcontroller node(ARS) Node 5
Brake valve
Supervisorycontroller node(ESP) Node 4
Front wheelcontroller node(AFS) Node 6
speed sensorFront wheel Rear wheel
steering drive
CAN
sensor nodeSteering wheel angle
Node 3
Front wheelangle sensor
Interfering nodeNode 1
Figure 3 IVC-NCS network structure
6 Journal of Sensors
0 2 4 6minus01
0
01
Time (s) Time (s)0 02 04 06 08 1
0
005
01
Ang
le (r
ad)
Ang
le (r
ad)
(a)
0 1 2 3 4 5 6minus04
minus02
0
02
04
Yaw
rate
(rad
s)
Time (s)
Ideal value
No controlIn control
(b)
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
Yaw
rate
(rad
s)
Time (s)
Ideal value
No controlIn control
(c)
Figure 4 (a) Input curve of front wheel angle (b)The response curve of yaw rate of the steering wheel with sine angle input (c)The responsecurve of yaw rate of the steering wheel with step angle input
the tracking response of the vehicle under different inputyaw rates According to the transmission ratio of the steeringsystem the corresponding input curve of front wheel steeringangle is shown in Figure 4(a) The vehicle travels at a goodroad with a adhesion coefficient of 085 and the initial speedis 25ms
Figures 4(b) and 4(c) are the response curves of vehicleyaw rate at different angle inputs It can be seen that the yawrate of the controlled vehicle can quickly and effectively trackthe ideal value when compared with the system without thecontrol For the sine input the execution of the vehicle is anonstandard single lane change test At this time due to thecorrection function of angle changes of the front wheel soafter the apparent slip the yaw rate is settled in zero value asshown in Figure 4(b)
Under the step input of steering wheel angle in Fig-ure 4(c) the yaw rate of the vehicle without control cannottrack the ideal value which appears as the trend of diver-gence So the vehicle cannot achieve stable circular motionand rollover because of instabilityThe yaw rate of the vehiclewith controllers is good at tracking the ideal value Simulationresults show that the IVC system can effectively improvethe stability vehicle in critical conditions which verifies theeffectiveness of the designed control system
52 Simulation Analysis of IVC-NCS Based on CAN In orderto investigate the performance change of the designed IVCsystem after the CAN network is involved in the control
the stability of the vehicle was investigated using the samestep input of the steering wheel The initial speed is 25msand the road adhesion coefficient is 085 Considering thepractical application of CAN network with high speed thecommunication rate is set to 250Kbps Node sends only dataframes If the interfering nodes do not send any messagethe network load is about 84 when the maximum is filledWhen the interference nodes send the interference messageof high priority with 4ms cycle it can ensure that the networkload is close to 1 but less than the network bandwidth whichensures the system communication not to lose the frames
According to the assumptions and simulation conditionsFigure 5 shows the comparison curve of yaw rate trackingaboutCANnetwork communication and point-to-point con-nection Compared with point-to-point connection modethe IVC system with CAN network connection can quicklyand effectively track the ideal value under the conditionof good network environment without changing the steadystate of the control system It can be clearly seen that inthe part of the amplified image the network involves inthe control system which makes the yaw rate fluctuate withmicroamplitude The overshoot of control increases from31 of the point-to-point connection to 6 of the CANnetwork connection
In order to investigate the influence of different networkstate on the control performance of the system the trackingsimulation test of vehicle yaw rate is carried out for differentnetwork load and packet loss rate
Journal of Sensors 7
0 01 02 03 04 05 06 07 08 09 10
00501
01502
02503
03504
035 04 045 05 055032034036038
Yaw
rate
(rad
s)
Time (s)
Ideal value
CANPoint to point
Figure 5 The response curve of IVC yaw rate of CAN networkconnection
Figure 6 shows the response curve of different packet lossrates of IVC-NCS yaw rate In the simulation process theinterference nodes do not send the messages It can be seenthat when the packet loss rate is lower than 20 the dynamiccharacteristic of the system becomes bad In the packet lossrate of 5 and 20 the corresponding overshoots of thesystem are about 9 and 125 In 03 s after the step input offrontwheel ends the vehicle yaw rate can be stable to track theideal valueWhen the packet loss rate is less than 40 the yawrate of the vehicle can be finally stabilized at an ideal valueWhen the packet loss rate is more than 40 the yaw rate isobviously fluctuated in the ideal yaw rate tracking process At50 the overshoot of yaw rate increases rapidly to about 42the vehicle begins to sideslip
When the packet loss rate is up to 60 the vehicleyaw rate tracking is seriously lagging behind which cannotachieve stable circular motion The analysis shows that whenthe packet loss rate is low themessage transmission keep highsuccess rate The information of the sensors can be obtainedby control nodes in time so the controller works fast withlittle effect on the performance of system control With theincrease of packet loss rate the control instructions cannotbe timely generated and executed which makes the controlcycle become longer The status of executing agency cannotbe corrected in time The input of executing agency will betoo large or too small which causes the control to fail
Figure 7 shows that the interference nodes send themessages of highest priority in 4ms cycle and the networkload is close to 1 The long dashes are the response curve ofyaw rate of CAN network without the interference when thenetwork load is about 84 The short dashed lines dashed-dotted lines and bold dashed lines are separately responsecurves of yaw rate at 119905 different packet loss rates when theload is full
Under the condition tomeet the communication require-ments of the control system when the network load isclose to 1 the induced delay of the system is largest It canbe calculated when the network load increases from 84to nearly 100 and the overshoot increases from 6 to 7When the network load is 1 and packet loss rate is 30
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
Yaw
rate
(rad
s)
Time (s)
Ideal valuePoint to pointLoss 5Loss 20
Loss 40Loss 50Loss 60
Figure 6 The response curve of different packet loss rates
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
04 045 05 055 06034
036
038
04
Yaw
rate
(rad
s)
Time (s)
Ideal valuePoint to pointNo interference
No lossLoss 30Loss 50
Figure 7 The response curve of yaw rate with full load of networkcommunication
the overshoot of yaw rate is 157 Therefore although thenetwork load increases as long as network load can meetthe communication requirements of the control system thenetwork intervention only has little effect on the qualityof dynamic control which does not change the steadycharacteristics of the system The vehicle can achieve thestable circular motion within 03 s of the yaw rare input ofthe front wheel
When the communication network is fully loaded andthe packet loss rate is 50 the vehicle cannot completethe scheduled circular motion The yaw rate of the vehiclediverges to make the vehicle out of control The simulationresults show that when the network bandwidth meets theneeds of control system the effect of the network induceddelay of control system is very small and negligible And thenetwork packet loss will affect the performance of controlsystem seriously When the packet loss rate is up to 50 thesystem control performance will deteriorate significantly
8 Journal of Sensors
53 Stability and Coordination Analysis From the devel-opment of the vehicle chassis control system the trend ofintegration and network is very obvious The system controlarchitecture and the network architecture form have differenteffects on the stability control of the chassis In this paperthe design of the control system fully takes into account thestability of the chassis control performance
Because ABS is the basis for the realization of ESPand the latter needs to achieve the independent control ofbraking intensity about the four wheels so ABS is designedas an independent four-channel mode As one kind of thecontroller associated with safety and real-time the executionand controller of ABS usually adopt directly connectedmanner in order to reduce the information switching delayand ensure the safety and stability of the vehicle
The control target of ESP system is to control the stabilityof the vehicle in the extreme conditions through the controlof braking strength of four wheels to achieve the activesafety In order to improve the unstable state of the vehiclein extreme conditions applying the braking force on inwardrear wheel with the understeer or on the outward front wheelwith the oversteer can quickly and effectively improve thestability Taking into account that ESP system has the obviouseffect on the vehicle stability in extreme conditions thestudy will assign the supervision and control tasks and thecalculation of ESP control to one node
For the performance of network control system commu-nication real-time performance is the most important factoraffecting the control performance which can be expressedand measured by network delay The existence of networkdelay reduces the control performance of the system whichwill lead to the loss of stability of the stable control system
Especially in extreme conditions the change of thevehicle state is largerWhen a large number of control instruc-tions are lost the adjustment of the new and old controlinstructions is bound to increase because of the large numberof cycles which will increase the action range of the actuatorTherefore toomuch data packet loss is extremely unfavorablefor the stability control When the packet loss rate is lessthan a certain value only the system dynamic characteristicbecomesworse and the system stability is not changedWhenthe packet loss rate reaches the critical value the systemcontrol stability is close to the critical state
In addition through the simulation experiment we canknow that CAN network intervention did not significantlyaffect the stability of vehicle braking Therefore when CANnetwork communication environment is good the networkinduced delay of CAN network has a little influence on theperformance of the controller which indicates that the ABScontroller built in this research has strong robustness on asingle road
In the 80s of last century the researchers began to tryto decompose the complex chassis control problem into anumber of subcontrol systems and then use a mechanism tocoordinate the dynamic relationship between the subsys-tems to meet the control requirements Using the uppercoordinated control for the integrated control architectureof multiple independent control units of the vehicle chassiscan effectively adjust the collaborative work of the control
units avoid the conflict between the controllers and makethe vehicle obtain optimal running state
The supervision mechanism is based on a hierarchicalcontrol principle combinedwith fuzzy control logic to designa controller to supervise and coordinate ESP AFS and ARSThe target of the upper supervisory controller according tothe stability factor to judge vehicle steady state is to redistrib-ute the control weights of three subsystems and coordinatethe output of each subcontroller
The sensors necessary for many systems are designed asindependent network nodes The subcontrol systems adopttraditional point-to-point connection in the controllers sen-sors and executing agencies Its object is to obtain satisfactoryreal-time performance and coordination
6 Conclusions
In this paper the vehicle chassis control system is taken as theapplication of CAN network The target focuses on how thenetwork affects the control system The ABS ASC and IVCare simulated The main research contents and conclusionsare as follows
According to the control theory of sliding mode ESP andAFS subcontrollers are designed to track the ideal yaw rateBased on the principle of hierarchical control and fuzzy con-trol a fuzzy controller is designed which is used to monitorand coordinate the ESP AFS andARS And the IVC system isconstructed with the upper supervisory controller and threesubcontrol systems on the Simulink platform Comparedwith the point-to-point connection the system simulationof IVC-NCS shows that the control of the integrated controlsystem has good performance
According to the IVC based on the supervision mech-anism combined with the function of each subsystem thenetwork topology structure of IVC is proposed and theIVC communication matrix based on CAN network com-munication is designed With the common sensors andthe subcontrollers as the CAN network independent nodesthe network induced delay and packet loss rate on thesystem control performance are studied by simulation Thesimulation results show that the network does not lose frameand even if the network traffic load is close to 1 the networkintervention of IVC can only show the very small change ofthe dynamic quality of the system The network packet losshas a significant impact on the performance of the systemcontrol When the packet loss rate is less than 30 only thesystem dynamic performance becomes worse and the systemstability does not change When the packet loss rate is up to50 the system control stability is close to the critical stateand the vehicle is unstable
Competing Interests
The authors declare that they have no competing interests
Acknowledgments
This study was funded byThe Natural Science Foundation ofJiangsu Province (BK20130977)
Journal of Sensors 9
References
[1] T Gordon M Howell and F Brandao ldquoIntegrated controlmethodologies for road vehiclesrdquoVehicle System Dynamics vol40 no 1ndash3 pp 157ndash190 2003
[2] H Chou and B DrsquoAndrea-Novel ldquoGlobal vehicle control usingdifferential braking torques and active suspension forcesrdquoVehicle System Dynamics vol 43 no 4 pp 261ndash284 2005
[3] C B Chu and W W Chen ldquoVehicle chassis system based onlayered coordinated controlrdquo Chinese Journal of MechanicalEngineering vol 44 no 2 pp 157ndash162 2008
[4] H Zhu and W W Chen ldquoActive control of vehicle suspensionand steering system based on strategy hierarchyrdquo Chinese Jour-nal of Agricultural Machinery vol 39 no 10 pp 1ndash6 2008
[5] M J L Boada B L Boada A Munoz and V Diaz ldquoIntegratedcontrol of front-wheel steering and front braking forces on thebasis of fuzzy logicrdquo Proceedings of the Institution of MechanicalEngineers Part D Journal of Automobile Engineering vol 220no 3 pp 253ndash267 2006
[6] D Li X Shen and F Yu ldquoIntegrated vehicle chassis control witha mainservo-loop structurerdquo International Journal of Automo-tive Technology vol 7 no 7 pp 803ndash812 2006
[7] E J Bedner and H H Chen ldquoA supervisory control to managebrakes and four-wheel-steer systemsrdquo SAE Paper 2004-01-10592004
[8] S Chang and T J Gordon ldquoA flexible hierarchical model-basedcontrol methodology for vehicle active safety systemsrdquo VehicleSystem Dynamics vol 46 supplement 1 pp 63ndash75 2008
[9] N Kelling ldquoThe BRAKE projectmdashcentralized versus dis-tributed redundancy for brake-by-wire systemsrdquo SAE Paper2002-01-0266 SAE International 2002
[10] J X Wang Research of Integrated Control System of VehicleChassis Based on Multi Agent Southeast University NanjingChina 2010
[11] Y Zhang C L Yin and J W Zhang ldquoA real time estimationmethod for the lateral velocity of the center of mass of thevehiclerdquo Chinese Journal of Mechanical Engineering vol 44 no2 pp 219ndash222 2008
[12] D Crolla and Y Fan Vehicle Dynamics and Control ChineseCommunications Press Beijing China 2003
[13] H B Pacejka andE Bakker ldquoMagic formula tyremodelrdquoVehicleSystem Dynamics vol 21 no 1 pp 1ndash18 1993
[14] H B Pacejka and I J M Besselink ldquoMagic formula tyre modelwith transient propertiesrdquo Vehicle System Dynamics vol 27supplement 1 pp 234ndash249 1997
[15] K H Guo and L Ren ldquoA unifield semi-empirical tire modelwith higher accuracy and less parametersrdquo SAETechnical PaperSeries 1999-01-0785 SAE International 1999
[16] H Dugoff P S Fancher and L Segal ldquoTyre performance char-acteristics affecting vehicle response to steering and brakingcontrol inputsrdquo Final Report US National 1969
[17] W Jinxiang and C Nan ldquoResearch on supervisory controlbased integrated chassis control framework and its simulationrdquoTransactions of the Chinese Society of Agricultural Machineryvol 40 no 9 pp 1ndash6 2009
[18] S Inagaki I Kushiro and M Yamamoto ldquoAnalysis on vehiclestability in critical cornering using phase-plane methodrdquo inProceedings of the International Symposium on Advanced VehicleControl (AVEC rsquo94) pp 287ndash292 Tsukuba- Shi Japan 1994
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
6 Journal of Sensors
0 2 4 6minus01
0
01
Time (s) Time (s)0 02 04 06 08 1
0
005
01
Ang
le (r
ad)
Ang
le (r
ad)
(a)
0 1 2 3 4 5 6minus04
minus02
0
02
04
Yaw
rate
(rad
s)
Time (s)
Ideal value
No controlIn control
(b)
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
Yaw
rate
(rad
s)
Time (s)
Ideal value
No controlIn control
(c)
Figure 4 (a) Input curve of front wheel angle (b)The response curve of yaw rate of the steering wheel with sine angle input (c)The responsecurve of yaw rate of the steering wheel with step angle input
the tracking response of the vehicle under different inputyaw rates According to the transmission ratio of the steeringsystem the corresponding input curve of front wheel steeringangle is shown in Figure 4(a) The vehicle travels at a goodroad with a adhesion coefficient of 085 and the initial speedis 25ms
Figures 4(b) and 4(c) are the response curves of vehicleyaw rate at different angle inputs It can be seen that the yawrate of the controlled vehicle can quickly and effectively trackthe ideal value when compared with the system without thecontrol For the sine input the execution of the vehicle is anonstandard single lane change test At this time due to thecorrection function of angle changes of the front wheel soafter the apparent slip the yaw rate is settled in zero value asshown in Figure 4(b)
Under the step input of steering wheel angle in Fig-ure 4(c) the yaw rate of the vehicle without control cannottrack the ideal value which appears as the trend of diver-gence So the vehicle cannot achieve stable circular motionand rollover because of instabilityThe yaw rate of the vehiclewith controllers is good at tracking the ideal value Simulationresults show that the IVC system can effectively improvethe stability vehicle in critical conditions which verifies theeffectiveness of the designed control system
52 Simulation Analysis of IVC-NCS Based on CAN In orderto investigate the performance change of the designed IVCsystem after the CAN network is involved in the control
the stability of the vehicle was investigated using the samestep input of the steering wheel The initial speed is 25msand the road adhesion coefficient is 085 Considering thepractical application of CAN network with high speed thecommunication rate is set to 250Kbps Node sends only dataframes If the interfering nodes do not send any messagethe network load is about 84 when the maximum is filledWhen the interference nodes send the interference messageof high priority with 4ms cycle it can ensure that the networkload is close to 1 but less than the network bandwidth whichensures the system communication not to lose the frames
According to the assumptions and simulation conditionsFigure 5 shows the comparison curve of yaw rate trackingaboutCANnetwork communication and point-to-point con-nection Compared with point-to-point connection modethe IVC system with CAN network connection can quicklyand effectively track the ideal value under the conditionof good network environment without changing the steadystate of the control system It can be clearly seen that inthe part of the amplified image the network involves inthe control system which makes the yaw rate fluctuate withmicroamplitude The overshoot of control increases from31 of the point-to-point connection to 6 of the CANnetwork connection
In order to investigate the influence of different networkstate on the control performance of the system the trackingsimulation test of vehicle yaw rate is carried out for differentnetwork load and packet loss rate
Journal of Sensors 7
0 01 02 03 04 05 06 07 08 09 10
00501
01502
02503
03504
035 04 045 05 055032034036038
Yaw
rate
(rad
s)
Time (s)
Ideal value
CANPoint to point
Figure 5 The response curve of IVC yaw rate of CAN networkconnection
Figure 6 shows the response curve of different packet lossrates of IVC-NCS yaw rate In the simulation process theinterference nodes do not send the messages It can be seenthat when the packet loss rate is lower than 20 the dynamiccharacteristic of the system becomes bad In the packet lossrate of 5 and 20 the corresponding overshoots of thesystem are about 9 and 125 In 03 s after the step input offrontwheel ends the vehicle yaw rate can be stable to track theideal valueWhen the packet loss rate is less than 40 the yawrate of the vehicle can be finally stabilized at an ideal valueWhen the packet loss rate is more than 40 the yaw rate isobviously fluctuated in the ideal yaw rate tracking process At50 the overshoot of yaw rate increases rapidly to about 42the vehicle begins to sideslip
When the packet loss rate is up to 60 the vehicleyaw rate tracking is seriously lagging behind which cannotachieve stable circular motion The analysis shows that whenthe packet loss rate is low themessage transmission keep highsuccess rate The information of the sensors can be obtainedby control nodes in time so the controller works fast withlittle effect on the performance of system control With theincrease of packet loss rate the control instructions cannotbe timely generated and executed which makes the controlcycle become longer The status of executing agency cannotbe corrected in time The input of executing agency will betoo large or too small which causes the control to fail
Figure 7 shows that the interference nodes send themessages of highest priority in 4ms cycle and the networkload is close to 1 The long dashes are the response curve ofyaw rate of CAN network without the interference when thenetwork load is about 84 The short dashed lines dashed-dotted lines and bold dashed lines are separately responsecurves of yaw rate at 119905 different packet loss rates when theload is full
Under the condition tomeet the communication require-ments of the control system when the network load isclose to 1 the induced delay of the system is largest It canbe calculated when the network load increases from 84to nearly 100 and the overshoot increases from 6 to 7When the network load is 1 and packet loss rate is 30
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
Yaw
rate
(rad
s)
Time (s)
Ideal valuePoint to pointLoss 5Loss 20
Loss 40Loss 50Loss 60
Figure 6 The response curve of different packet loss rates
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
04 045 05 055 06034
036
038
04
Yaw
rate
(rad
s)
Time (s)
Ideal valuePoint to pointNo interference
No lossLoss 30Loss 50
Figure 7 The response curve of yaw rate with full load of networkcommunication
the overshoot of yaw rate is 157 Therefore although thenetwork load increases as long as network load can meetthe communication requirements of the control system thenetwork intervention only has little effect on the qualityof dynamic control which does not change the steadycharacteristics of the system The vehicle can achieve thestable circular motion within 03 s of the yaw rare input ofthe front wheel
When the communication network is fully loaded andthe packet loss rate is 50 the vehicle cannot completethe scheduled circular motion The yaw rate of the vehiclediverges to make the vehicle out of control The simulationresults show that when the network bandwidth meets theneeds of control system the effect of the network induceddelay of control system is very small and negligible And thenetwork packet loss will affect the performance of controlsystem seriously When the packet loss rate is up to 50 thesystem control performance will deteriorate significantly
8 Journal of Sensors
53 Stability and Coordination Analysis From the devel-opment of the vehicle chassis control system the trend ofintegration and network is very obvious The system controlarchitecture and the network architecture form have differenteffects on the stability control of the chassis In this paperthe design of the control system fully takes into account thestability of the chassis control performance
Because ABS is the basis for the realization of ESPand the latter needs to achieve the independent control ofbraking intensity about the four wheels so ABS is designedas an independent four-channel mode As one kind of thecontroller associated with safety and real-time the executionand controller of ABS usually adopt directly connectedmanner in order to reduce the information switching delayand ensure the safety and stability of the vehicle
The control target of ESP system is to control the stabilityof the vehicle in the extreme conditions through the controlof braking strength of four wheels to achieve the activesafety In order to improve the unstable state of the vehiclein extreme conditions applying the braking force on inwardrear wheel with the understeer or on the outward front wheelwith the oversteer can quickly and effectively improve thestability Taking into account that ESP system has the obviouseffect on the vehicle stability in extreme conditions thestudy will assign the supervision and control tasks and thecalculation of ESP control to one node
For the performance of network control system commu-nication real-time performance is the most important factoraffecting the control performance which can be expressedand measured by network delay The existence of networkdelay reduces the control performance of the system whichwill lead to the loss of stability of the stable control system
Especially in extreme conditions the change of thevehicle state is largerWhen a large number of control instruc-tions are lost the adjustment of the new and old controlinstructions is bound to increase because of the large numberof cycles which will increase the action range of the actuatorTherefore toomuch data packet loss is extremely unfavorablefor the stability control When the packet loss rate is lessthan a certain value only the system dynamic characteristicbecomesworse and the system stability is not changedWhenthe packet loss rate reaches the critical value the systemcontrol stability is close to the critical state
In addition through the simulation experiment we canknow that CAN network intervention did not significantlyaffect the stability of vehicle braking Therefore when CANnetwork communication environment is good the networkinduced delay of CAN network has a little influence on theperformance of the controller which indicates that the ABScontroller built in this research has strong robustness on asingle road
In the 80s of last century the researchers began to tryto decompose the complex chassis control problem into anumber of subcontrol systems and then use a mechanism tocoordinate the dynamic relationship between the subsys-tems to meet the control requirements Using the uppercoordinated control for the integrated control architectureof multiple independent control units of the vehicle chassiscan effectively adjust the collaborative work of the control
units avoid the conflict between the controllers and makethe vehicle obtain optimal running state
The supervision mechanism is based on a hierarchicalcontrol principle combinedwith fuzzy control logic to designa controller to supervise and coordinate ESP AFS and ARSThe target of the upper supervisory controller according tothe stability factor to judge vehicle steady state is to redistrib-ute the control weights of three subsystems and coordinatethe output of each subcontroller
The sensors necessary for many systems are designed asindependent network nodes The subcontrol systems adopttraditional point-to-point connection in the controllers sen-sors and executing agencies Its object is to obtain satisfactoryreal-time performance and coordination
6 Conclusions
In this paper the vehicle chassis control system is taken as theapplication of CAN network The target focuses on how thenetwork affects the control system The ABS ASC and IVCare simulated The main research contents and conclusionsare as follows
According to the control theory of sliding mode ESP andAFS subcontrollers are designed to track the ideal yaw rateBased on the principle of hierarchical control and fuzzy con-trol a fuzzy controller is designed which is used to monitorand coordinate the ESP AFS andARS And the IVC system isconstructed with the upper supervisory controller and threesubcontrol systems on the Simulink platform Comparedwith the point-to-point connection the system simulationof IVC-NCS shows that the control of the integrated controlsystem has good performance
According to the IVC based on the supervision mech-anism combined with the function of each subsystem thenetwork topology structure of IVC is proposed and theIVC communication matrix based on CAN network com-munication is designed With the common sensors andthe subcontrollers as the CAN network independent nodesthe network induced delay and packet loss rate on thesystem control performance are studied by simulation Thesimulation results show that the network does not lose frameand even if the network traffic load is close to 1 the networkintervention of IVC can only show the very small change ofthe dynamic quality of the system The network packet losshas a significant impact on the performance of the systemcontrol When the packet loss rate is less than 30 only thesystem dynamic performance becomes worse and the systemstability does not change When the packet loss rate is up to50 the system control stability is close to the critical stateand the vehicle is unstable
Competing Interests
The authors declare that they have no competing interests
Acknowledgments
This study was funded byThe Natural Science Foundation ofJiangsu Province (BK20130977)
Journal of Sensors 9
References
[1] T Gordon M Howell and F Brandao ldquoIntegrated controlmethodologies for road vehiclesrdquoVehicle System Dynamics vol40 no 1ndash3 pp 157ndash190 2003
[2] H Chou and B DrsquoAndrea-Novel ldquoGlobal vehicle control usingdifferential braking torques and active suspension forcesrdquoVehicle System Dynamics vol 43 no 4 pp 261ndash284 2005
[3] C B Chu and W W Chen ldquoVehicle chassis system based onlayered coordinated controlrdquo Chinese Journal of MechanicalEngineering vol 44 no 2 pp 157ndash162 2008
[4] H Zhu and W W Chen ldquoActive control of vehicle suspensionand steering system based on strategy hierarchyrdquo Chinese Jour-nal of Agricultural Machinery vol 39 no 10 pp 1ndash6 2008
[5] M J L Boada B L Boada A Munoz and V Diaz ldquoIntegratedcontrol of front-wheel steering and front braking forces on thebasis of fuzzy logicrdquo Proceedings of the Institution of MechanicalEngineers Part D Journal of Automobile Engineering vol 220no 3 pp 253ndash267 2006
[6] D Li X Shen and F Yu ldquoIntegrated vehicle chassis control witha mainservo-loop structurerdquo International Journal of Automo-tive Technology vol 7 no 7 pp 803ndash812 2006
[7] E J Bedner and H H Chen ldquoA supervisory control to managebrakes and four-wheel-steer systemsrdquo SAE Paper 2004-01-10592004
[8] S Chang and T J Gordon ldquoA flexible hierarchical model-basedcontrol methodology for vehicle active safety systemsrdquo VehicleSystem Dynamics vol 46 supplement 1 pp 63ndash75 2008
[9] N Kelling ldquoThe BRAKE projectmdashcentralized versus dis-tributed redundancy for brake-by-wire systemsrdquo SAE Paper2002-01-0266 SAE International 2002
[10] J X Wang Research of Integrated Control System of VehicleChassis Based on Multi Agent Southeast University NanjingChina 2010
[11] Y Zhang C L Yin and J W Zhang ldquoA real time estimationmethod for the lateral velocity of the center of mass of thevehiclerdquo Chinese Journal of Mechanical Engineering vol 44 no2 pp 219ndash222 2008
[12] D Crolla and Y Fan Vehicle Dynamics and Control ChineseCommunications Press Beijing China 2003
[13] H B Pacejka andE Bakker ldquoMagic formula tyremodelrdquoVehicleSystem Dynamics vol 21 no 1 pp 1ndash18 1993
[14] H B Pacejka and I J M Besselink ldquoMagic formula tyre modelwith transient propertiesrdquo Vehicle System Dynamics vol 27supplement 1 pp 234ndash249 1997
[15] K H Guo and L Ren ldquoA unifield semi-empirical tire modelwith higher accuracy and less parametersrdquo SAETechnical PaperSeries 1999-01-0785 SAE International 1999
[16] H Dugoff P S Fancher and L Segal ldquoTyre performance char-acteristics affecting vehicle response to steering and brakingcontrol inputsrdquo Final Report US National 1969
[17] W Jinxiang and C Nan ldquoResearch on supervisory controlbased integrated chassis control framework and its simulationrdquoTransactions of the Chinese Society of Agricultural Machineryvol 40 no 9 pp 1ndash6 2009
[18] S Inagaki I Kushiro and M Yamamoto ldquoAnalysis on vehiclestability in critical cornering using phase-plane methodrdquo inProceedings of the International Symposium on Advanced VehicleControl (AVEC rsquo94) pp 287ndash292 Tsukuba- Shi Japan 1994
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
Journal of Sensors 7
0 01 02 03 04 05 06 07 08 09 10
00501
01502
02503
03504
035 04 045 05 055032034036038
Yaw
rate
(rad
s)
Time (s)
Ideal value
CANPoint to point
Figure 5 The response curve of IVC yaw rate of CAN networkconnection
Figure 6 shows the response curve of different packet lossrates of IVC-NCS yaw rate In the simulation process theinterference nodes do not send the messages It can be seenthat when the packet loss rate is lower than 20 the dynamiccharacteristic of the system becomes bad In the packet lossrate of 5 and 20 the corresponding overshoots of thesystem are about 9 and 125 In 03 s after the step input offrontwheel ends the vehicle yaw rate can be stable to track theideal valueWhen the packet loss rate is less than 40 the yawrate of the vehicle can be finally stabilized at an ideal valueWhen the packet loss rate is more than 40 the yaw rate isobviously fluctuated in the ideal yaw rate tracking process At50 the overshoot of yaw rate increases rapidly to about 42the vehicle begins to sideslip
When the packet loss rate is up to 60 the vehicleyaw rate tracking is seriously lagging behind which cannotachieve stable circular motion The analysis shows that whenthe packet loss rate is low themessage transmission keep highsuccess rate The information of the sensors can be obtainedby control nodes in time so the controller works fast withlittle effect on the performance of system control With theincrease of packet loss rate the control instructions cannotbe timely generated and executed which makes the controlcycle become longer The status of executing agency cannotbe corrected in time The input of executing agency will betoo large or too small which causes the control to fail
Figure 7 shows that the interference nodes send themessages of highest priority in 4ms cycle and the networkload is close to 1 The long dashes are the response curve ofyaw rate of CAN network without the interference when thenetwork load is about 84 The short dashed lines dashed-dotted lines and bold dashed lines are separately responsecurves of yaw rate at 119905 different packet loss rates when theload is full
Under the condition tomeet the communication require-ments of the control system when the network load isclose to 1 the induced delay of the system is largest It canbe calculated when the network load increases from 84to nearly 100 and the overshoot increases from 6 to 7When the network load is 1 and packet loss rate is 30
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
Yaw
rate
(rad
s)
Time (s)
Ideal valuePoint to pointLoss 5Loss 20
Loss 40Loss 50Loss 60
Figure 6 The response curve of different packet loss rates
0 01 02 03 04 05 06 07 08 09 10
01
02
03
04
05
04 045 05 055 06034
036
038
04
Yaw
rate
(rad
s)
Time (s)
Ideal valuePoint to pointNo interference
No lossLoss 30Loss 50
Figure 7 The response curve of yaw rate with full load of networkcommunication
the overshoot of yaw rate is 157 Therefore although thenetwork load increases as long as network load can meetthe communication requirements of the control system thenetwork intervention only has little effect on the qualityof dynamic control which does not change the steadycharacteristics of the system The vehicle can achieve thestable circular motion within 03 s of the yaw rare input ofthe front wheel
When the communication network is fully loaded andthe packet loss rate is 50 the vehicle cannot completethe scheduled circular motion The yaw rate of the vehiclediverges to make the vehicle out of control The simulationresults show that when the network bandwidth meets theneeds of control system the effect of the network induceddelay of control system is very small and negligible And thenetwork packet loss will affect the performance of controlsystem seriously When the packet loss rate is up to 50 thesystem control performance will deteriorate significantly
8 Journal of Sensors
53 Stability and Coordination Analysis From the devel-opment of the vehicle chassis control system the trend ofintegration and network is very obvious The system controlarchitecture and the network architecture form have differenteffects on the stability control of the chassis In this paperthe design of the control system fully takes into account thestability of the chassis control performance
Because ABS is the basis for the realization of ESPand the latter needs to achieve the independent control ofbraking intensity about the four wheels so ABS is designedas an independent four-channel mode As one kind of thecontroller associated with safety and real-time the executionand controller of ABS usually adopt directly connectedmanner in order to reduce the information switching delayand ensure the safety and stability of the vehicle
The control target of ESP system is to control the stabilityof the vehicle in the extreme conditions through the controlof braking strength of four wheels to achieve the activesafety In order to improve the unstable state of the vehiclein extreme conditions applying the braking force on inwardrear wheel with the understeer or on the outward front wheelwith the oversteer can quickly and effectively improve thestability Taking into account that ESP system has the obviouseffect on the vehicle stability in extreme conditions thestudy will assign the supervision and control tasks and thecalculation of ESP control to one node
For the performance of network control system commu-nication real-time performance is the most important factoraffecting the control performance which can be expressedand measured by network delay The existence of networkdelay reduces the control performance of the system whichwill lead to the loss of stability of the stable control system
Especially in extreme conditions the change of thevehicle state is largerWhen a large number of control instruc-tions are lost the adjustment of the new and old controlinstructions is bound to increase because of the large numberof cycles which will increase the action range of the actuatorTherefore toomuch data packet loss is extremely unfavorablefor the stability control When the packet loss rate is lessthan a certain value only the system dynamic characteristicbecomesworse and the system stability is not changedWhenthe packet loss rate reaches the critical value the systemcontrol stability is close to the critical state
In addition through the simulation experiment we canknow that CAN network intervention did not significantlyaffect the stability of vehicle braking Therefore when CANnetwork communication environment is good the networkinduced delay of CAN network has a little influence on theperformance of the controller which indicates that the ABScontroller built in this research has strong robustness on asingle road
In the 80s of last century the researchers began to tryto decompose the complex chassis control problem into anumber of subcontrol systems and then use a mechanism tocoordinate the dynamic relationship between the subsys-tems to meet the control requirements Using the uppercoordinated control for the integrated control architectureof multiple independent control units of the vehicle chassiscan effectively adjust the collaborative work of the control
units avoid the conflict between the controllers and makethe vehicle obtain optimal running state
The supervision mechanism is based on a hierarchicalcontrol principle combinedwith fuzzy control logic to designa controller to supervise and coordinate ESP AFS and ARSThe target of the upper supervisory controller according tothe stability factor to judge vehicle steady state is to redistrib-ute the control weights of three subsystems and coordinatethe output of each subcontroller
The sensors necessary for many systems are designed asindependent network nodes The subcontrol systems adopttraditional point-to-point connection in the controllers sen-sors and executing agencies Its object is to obtain satisfactoryreal-time performance and coordination
6 Conclusions
In this paper the vehicle chassis control system is taken as theapplication of CAN network The target focuses on how thenetwork affects the control system The ABS ASC and IVCare simulated The main research contents and conclusionsare as follows
According to the control theory of sliding mode ESP andAFS subcontrollers are designed to track the ideal yaw rateBased on the principle of hierarchical control and fuzzy con-trol a fuzzy controller is designed which is used to monitorand coordinate the ESP AFS andARS And the IVC system isconstructed with the upper supervisory controller and threesubcontrol systems on the Simulink platform Comparedwith the point-to-point connection the system simulationof IVC-NCS shows that the control of the integrated controlsystem has good performance
According to the IVC based on the supervision mech-anism combined with the function of each subsystem thenetwork topology structure of IVC is proposed and theIVC communication matrix based on CAN network com-munication is designed With the common sensors andthe subcontrollers as the CAN network independent nodesthe network induced delay and packet loss rate on thesystem control performance are studied by simulation Thesimulation results show that the network does not lose frameand even if the network traffic load is close to 1 the networkintervention of IVC can only show the very small change ofthe dynamic quality of the system The network packet losshas a significant impact on the performance of the systemcontrol When the packet loss rate is less than 30 only thesystem dynamic performance becomes worse and the systemstability does not change When the packet loss rate is up to50 the system control stability is close to the critical stateand the vehicle is unstable
Competing Interests
The authors declare that they have no competing interests
Acknowledgments
This study was funded byThe Natural Science Foundation ofJiangsu Province (BK20130977)
Journal of Sensors 9
References
[1] T Gordon M Howell and F Brandao ldquoIntegrated controlmethodologies for road vehiclesrdquoVehicle System Dynamics vol40 no 1ndash3 pp 157ndash190 2003
[2] H Chou and B DrsquoAndrea-Novel ldquoGlobal vehicle control usingdifferential braking torques and active suspension forcesrdquoVehicle System Dynamics vol 43 no 4 pp 261ndash284 2005
[3] C B Chu and W W Chen ldquoVehicle chassis system based onlayered coordinated controlrdquo Chinese Journal of MechanicalEngineering vol 44 no 2 pp 157ndash162 2008
[4] H Zhu and W W Chen ldquoActive control of vehicle suspensionand steering system based on strategy hierarchyrdquo Chinese Jour-nal of Agricultural Machinery vol 39 no 10 pp 1ndash6 2008
[5] M J L Boada B L Boada A Munoz and V Diaz ldquoIntegratedcontrol of front-wheel steering and front braking forces on thebasis of fuzzy logicrdquo Proceedings of the Institution of MechanicalEngineers Part D Journal of Automobile Engineering vol 220no 3 pp 253ndash267 2006
[6] D Li X Shen and F Yu ldquoIntegrated vehicle chassis control witha mainservo-loop structurerdquo International Journal of Automo-tive Technology vol 7 no 7 pp 803ndash812 2006
[7] E J Bedner and H H Chen ldquoA supervisory control to managebrakes and four-wheel-steer systemsrdquo SAE Paper 2004-01-10592004
[8] S Chang and T J Gordon ldquoA flexible hierarchical model-basedcontrol methodology for vehicle active safety systemsrdquo VehicleSystem Dynamics vol 46 supplement 1 pp 63ndash75 2008
[9] N Kelling ldquoThe BRAKE projectmdashcentralized versus dis-tributed redundancy for brake-by-wire systemsrdquo SAE Paper2002-01-0266 SAE International 2002
[10] J X Wang Research of Integrated Control System of VehicleChassis Based on Multi Agent Southeast University NanjingChina 2010
[11] Y Zhang C L Yin and J W Zhang ldquoA real time estimationmethod for the lateral velocity of the center of mass of thevehiclerdquo Chinese Journal of Mechanical Engineering vol 44 no2 pp 219ndash222 2008
[12] D Crolla and Y Fan Vehicle Dynamics and Control ChineseCommunications Press Beijing China 2003
[13] H B Pacejka andE Bakker ldquoMagic formula tyremodelrdquoVehicleSystem Dynamics vol 21 no 1 pp 1ndash18 1993
[14] H B Pacejka and I J M Besselink ldquoMagic formula tyre modelwith transient propertiesrdquo Vehicle System Dynamics vol 27supplement 1 pp 234ndash249 1997
[15] K H Guo and L Ren ldquoA unifield semi-empirical tire modelwith higher accuracy and less parametersrdquo SAETechnical PaperSeries 1999-01-0785 SAE International 1999
[16] H Dugoff P S Fancher and L Segal ldquoTyre performance char-acteristics affecting vehicle response to steering and brakingcontrol inputsrdquo Final Report US National 1969
[17] W Jinxiang and C Nan ldquoResearch on supervisory controlbased integrated chassis control framework and its simulationrdquoTransactions of the Chinese Society of Agricultural Machineryvol 40 no 9 pp 1ndash6 2009
[18] S Inagaki I Kushiro and M Yamamoto ldquoAnalysis on vehiclestability in critical cornering using phase-plane methodrdquo inProceedings of the International Symposium on Advanced VehicleControl (AVEC rsquo94) pp 287ndash292 Tsukuba- Shi Japan 1994
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
8 Journal of Sensors
53 Stability and Coordination Analysis From the devel-opment of the vehicle chassis control system the trend ofintegration and network is very obvious The system controlarchitecture and the network architecture form have differenteffects on the stability control of the chassis In this paperthe design of the control system fully takes into account thestability of the chassis control performance
Because ABS is the basis for the realization of ESPand the latter needs to achieve the independent control ofbraking intensity about the four wheels so ABS is designedas an independent four-channel mode As one kind of thecontroller associated with safety and real-time the executionand controller of ABS usually adopt directly connectedmanner in order to reduce the information switching delayand ensure the safety and stability of the vehicle
The control target of ESP system is to control the stabilityof the vehicle in the extreme conditions through the controlof braking strength of four wheels to achieve the activesafety In order to improve the unstable state of the vehiclein extreme conditions applying the braking force on inwardrear wheel with the understeer or on the outward front wheelwith the oversteer can quickly and effectively improve thestability Taking into account that ESP system has the obviouseffect on the vehicle stability in extreme conditions thestudy will assign the supervision and control tasks and thecalculation of ESP control to one node
For the performance of network control system commu-nication real-time performance is the most important factoraffecting the control performance which can be expressedand measured by network delay The existence of networkdelay reduces the control performance of the system whichwill lead to the loss of stability of the stable control system
Especially in extreme conditions the change of thevehicle state is largerWhen a large number of control instruc-tions are lost the adjustment of the new and old controlinstructions is bound to increase because of the large numberof cycles which will increase the action range of the actuatorTherefore toomuch data packet loss is extremely unfavorablefor the stability control When the packet loss rate is lessthan a certain value only the system dynamic characteristicbecomesworse and the system stability is not changedWhenthe packet loss rate reaches the critical value the systemcontrol stability is close to the critical state
In addition through the simulation experiment we canknow that CAN network intervention did not significantlyaffect the stability of vehicle braking Therefore when CANnetwork communication environment is good the networkinduced delay of CAN network has a little influence on theperformance of the controller which indicates that the ABScontroller built in this research has strong robustness on asingle road
In the 80s of last century the researchers began to tryto decompose the complex chassis control problem into anumber of subcontrol systems and then use a mechanism tocoordinate the dynamic relationship between the subsys-tems to meet the control requirements Using the uppercoordinated control for the integrated control architectureof multiple independent control units of the vehicle chassiscan effectively adjust the collaborative work of the control
units avoid the conflict between the controllers and makethe vehicle obtain optimal running state
The supervision mechanism is based on a hierarchicalcontrol principle combinedwith fuzzy control logic to designa controller to supervise and coordinate ESP AFS and ARSThe target of the upper supervisory controller according tothe stability factor to judge vehicle steady state is to redistrib-ute the control weights of three subsystems and coordinatethe output of each subcontroller
The sensors necessary for many systems are designed asindependent network nodes The subcontrol systems adopttraditional point-to-point connection in the controllers sen-sors and executing agencies Its object is to obtain satisfactoryreal-time performance and coordination
6 Conclusions
In this paper the vehicle chassis control system is taken as theapplication of CAN network The target focuses on how thenetwork affects the control system The ABS ASC and IVCare simulated The main research contents and conclusionsare as follows
According to the control theory of sliding mode ESP andAFS subcontrollers are designed to track the ideal yaw rateBased on the principle of hierarchical control and fuzzy con-trol a fuzzy controller is designed which is used to monitorand coordinate the ESP AFS andARS And the IVC system isconstructed with the upper supervisory controller and threesubcontrol systems on the Simulink platform Comparedwith the point-to-point connection the system simulationof IVC-NCS shows that the control of the integrated controlsystem has good performance
According to the IVC based on the supervision mech-anism combined with the function of each subsystem thenetwork topology structure of IVC is proposed and theIVC communication matrix based on CAN network com-munication is designed With the common sensors andthe subcontrollers as the CAN network independent nodesthe network induced delay and packet loss rate on thesystem control performance are studied by simulation Thesimulation results show that the network does not lose frameand even if the network traffic load is close to 1 the networkintervention of IVC can only show the very small change ofthe dynamic quality of the system The network packet losshas a significant impact on the performance of the systemcontrol When the packet loss rate is less than 30 only thesystem dynamic performance becomes worse and the systemstability does not change When the packet loss rate is up to50 the system control stability is close to the critical stateand the vehicle is unstable
Competing Interests
The authors declare that they have no competing interests
Acknowledgments
This study was funded byThe Natural Science Foundation ofJiangsu Province (BK20130977)
Journal of Sensors 9
References
[1] T Gordon M Howell and F Brandao ldquoIntegrated controlmethodologies for road vehiclesrdquoVehicle System Dynamics vol40 no 1ndash3 pp 157ndash190 2003
[2] H Chou and B DrsquoAndrea-Novel ldquoGlobal vehicle control usingdifferential braking torques and active suspension forcesrdquoVehicle System Dynamics vol 43 no 4 pp 261ndash284 2005
[3] C B Chu and W W Chen ldquoVehicle chassis system based onlayered coordinated controlrdquo Chinese Journal of MechanicalEngineering vol 44 no 2 pp 157ndash162 2008
[4] H Zhu and W W Chen ldquoActive control of vehicle suspensionand steering system based on strategy hierarchyrdquo Chinese Jour-nal of Agricultural Machinery vol 39 no 10 pp 1ndash6 2008
[5] M J L Boada B L Boada A Munoz and V Diaz ldquoIntegratedcontrol of front-wheel steering and front braking forces on thebasis of fuzzy logicrdquo Proceedings of the Institution of MechanicalEngineers Part D Journal of Automobile Engineering vol 220no 3 pp 253ndash267 2006
[6] D Li X Shen and F Yu ldquoIntegrated vehicle chassis control witha mainservo-loop structurerdquo International Journal of Automo-tive Technology vol 7 no 7 pp 803ndash812 2006
[7] E J Bedner and H H Chen ldquoA supervisory control to managebrakes and four-wheel-steer systemsrdquo SAE Paper 2004-01-10592004
[8] S Chang and T J Gordon ldquoA flexible hierarchical model-basedcontrol methodology for vehicle active safety systemsrdquo VehicleSystem Dynamics vol 46 supplement 1 pp 63ndash75 2008
[9] N Kelling ldquoThe BRAKE projectmdashcentralized versus dis-tributed redundancy for brake-by-wire systemsrdquo SAE Paper2002-01-0266 SAE International 2002
[10] J X Wang Research of Integrated Control System of VehicleChassis Based on Multi Agent Southeast University NanjingChina 2010
[11] Y Zhang C L Yin and J W Zhang ldquoA real time estimationmethod for the lateral velocity of the center of mass of thevehiclerdquo Chinese Journal of Mechanical Engineering vol 44 no2 pp 219ndash222 2008
[12] D Crolla and Y Fan Vehicle Dynamics and Control ChineseCommunications Press Beijing China 2003
[13] H B Pacejka andE Bakker ldquoMagic formula tyremodelrdquoVehicleSystem Dynamics vol 21 no 1 pp 1ndash18 1993
[14] H B Pacejka and I J M Besselink ldquoMagic formula tyre modelwith transient propertiesrdquo Vehicle System Dynamics vol 27supplement 1 pp 234ndash249 1997
[15] K H Guo and L Ren ldquoA unifield semi-empirical tire modelwith higher accuracy and less parametersrdquo SAETechnical PaperSeries 1999-01-0785 SAE International 1999
[16] H Dugoff P S Fancher and L Segal ldquoTyre performance char-acteristics affecting vehicle response to steering and brakingcontrol inputsrdquo Final Report US National 1969
[17] W Jinxiang and C Nan ldquoResearch on supervisory controlbased integrated chassis control framework and its simulationrdquoTransactions of the Chinese Society of Agricultural Machineryvol 40 no 9 pp 1ndash6 2009
[18] S Inagaki I Kushiro and M Yamamoto ldquoAnalysis on vehiclestability in critical cornering using phase-plane methodrdquo inProceedings of the International Symposium on Advanced VehicleControl (AVEC rsquo94) pp 287ndash292 Tsukuba- Shi Japan 1994
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
Journal of Sensors 9
References
[1] T Gordon M Howell and F Brandao ldquoIntegrated controlmethodologies for road vehiclesrdquoVehicle System Dynamics vol40 no 1ndash3 pp 157ndash190 2003
[2] H Chou and B DrsquoAndrea-Novel ldquoGlobal vehicle control usingdifferential braking torques and active suspension forcesrdquoVehicle System Dynamics vol 43 no 4 pp 261ndash284 2005
[3] C B Chu and W W Chen ldquoVehicle chassis system based onlayered coordinated controlrdquo Chinese Journal of MechanicalEngineering vol 44 no 2 pp 157ndash162 2008
[4] H Zhu and W W Chen ldquoActive control of vehicle suspensionand steering system based on strategy hierarchyrdquo Chinese Jour-nal of Agricultural Machinery vol 39 no 10 pp 1ndash6 2008
[5] M J L Boada B L Boada A Munoz and V Diaz ldquoIntegratedcontrol of front-wheel steering and front braking forces on thebasis of fuzzy logicrdquo Proceedings of the Institution of MechanicalEngineers Part D Journal of Automobile Engineering vol 220no 3 pp 253ndash267 2006
[6] D Li X Shen and F Yu ldquoIntegrated vehicle chassis control witha mainservo-loop structurerdquo International Journal of Automo-tive Technology vol 7 no 7 pp 803ndash812 2006
[7] E J Bedner and H H Chen ldquoA supervisory control to managebrakes and four-wheel-steer systemsrdquo SAE Paper 2004-01-10592004
[8] S Chang and T J Gordon ldquoA flexible hierarchical model-basedcontrol methodology for vehicle active safety systemsrdquo VehicleSystem Dynamics vol 46 supplement 1 pp 63ndash75 2008
[9] N Kelling ldquoThe BRAKE projectmdashcentralized versus dis-tributed redundancy for brake-by-wire systemsrdquo SAE Paper2002-01-0266 SAE International 2002
[10] J X Wang Research of Integrated Control System of VehicleChassis Based on Multi Agent Southeast University NanjingChina 2010
[11] Y Zhang C L Yin and J W Zhang ldquoA real time estimationmethod for the lateral velocity of the center of mass of thevehiclerdquo Chinese Journal of Mechanical Engineering vol 44 no2 pp 219ndash222 2008
[12] D Crolla and Y Fan Vehicle Dynamics and Control ChineseCommunications Press Beijing China 2003
[13] H B Pacejka andE Bakker ldquoMagic formula tyremodelrdquoVehicleSystem Dynamics vol 21 no 1 pp 1ndash18 1993
[14] H B Pacejka and I J M Besselink ldquoMagic formula tyre modelwith transient propertiesrdquo Vehicle System Dynamics vol 27supplement 1 pp 234ndash249 1997
[15] K H Guo and L Ren ldquoA unifield semi-empirical tire modelwith higher accuracy and less parametersrdquo SAETechnical PaperSeries 1999-01-0785 SAE International 1999
[16] H Dugoff P S Fancher and L Segal ldquoTyre performance char-acteristics affecting vehicle response to steering and brakingcontrol inputsrdquo Final Report US National 1969
[17] W Jinxiang and C Nan ldquoResearch on supervisory controlbased integrated chassis control framework and its simulationrdquoTransactions of the Chinese Society of Agricultural Machineryvol 40 no 9 pp 1ndash6 2009
[18] S Inagaki I Kushiro and M Yamamoto ldquoAnalysis on vehiclestability in critical cornering using phase-plane methodrdquo inProceedings of the International Symposium on Advanced VehicleControl (AVEC rsquo94) pp 287ndash292 Tsukuba- Shi Japan 1994
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
