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Transcript of OTKA * HUNGARIAN SCIENTIFIC RESEARCH FUND INTELLIGENT CONTROL of MANUFACTURING SYSTEMS (OTKA T...
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1INTCONTMANSYST
OTKA* HUNGARIAN SCIENTIFIC RESEARCH FUND
INTELLIGENT CONTROL of MANUFACTURING SYSTEMS
(OTKA T 037525)
Professor J.SOMLO, Academical Doctor of Engineering
Budapest University of Technology and Economics
TOPIC:
Scientific leader:
*OTKA – Országos Tudományos Kutatási Alapprogramok
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INTCONTMANSYST 2
Research covers:
Optimization Problem of Manufacturing Systems
Hybrid Dynamical Approach for FMS Scheduling
Optimal Robot Motion Planning
The present demonstration material covers the last two topics
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INTCONTMANSYST 3
Hybrid Dynamical Approach for FMS Scheduling Literature Survey
Basic Results published in: Perkins J.R., Kumar P.R. “Stable, Distributed, Real-Time Scheduling of Flexible Manufacturing (Assembly) Disassembly Systems,” IEEE Transactions on Automatic Control. Vol. 34, No:2. February 1989, pp. 139-148.
Mathematical theories provided in: Matveev A.S., Savkin A.V., “Qualitative Theory of Hybrid Dynamical Systems,” Birkhauser 2000, pp. 348.
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INTCONTMANSYST 4
Hybrid Dynamical Approach for FMS Scheduling
DiscussionsThe good solution of scheduling is a key issue of the goodness of FMS actions effectiveness.
Today's practice of solution is usually based on heuristic methods giving GANTT diagrams determining processing times for series.
Hybrid Dynamical Approach changes the formulation of tasks. Demand rates are formulated for the inputs. Dynamic nonlinear determination of the process based on switching policies is possible.
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INTCONTMANSYST 5
Hybrid Dynamical Approach for FMS SchedulingResearch results
The results of the present research are:
hybrid dynamical approach provides effective, overlapping production for practical tasks
the proposed demand rate determination method makes possible to met practical goals
the developed computing and simulation systems make possible to determine the goodness of planning parameters (demand rates, initial auxiliary buffer contents, etc.)
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INTCONTMANSYST 6
New Ways in FMS Scheduling
By Prof. János SOMLO
Budapest University of Technology and Economics,
e-mail: [email protected]
Demonstration of Hybrid Dynamical Approach
for FMS Scheduling
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INTCONTMANSYST 7
Content
1. What is the Hybrid Dynamical Approach for FMS Scheduling
2. Scheduling Problem for FMS
3. Determination of Demand Rates
4. The Active Buffer Technique
5. Conclusions
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INTCONTMANSYST 8
1. What is the Hybrid Dynamical Approach to FMS Scheduling
Let us outline some basic points of HDA use. Consider a system given on Figure at the next page. This is a continuous analog of a part of a Flexible Manufacturing System. There are a number of tanks which are supplied with fluid. The incoming fluid flow rates are d1, d2, d3 ..etc. The servers are intelligent. It means that they
drop the content of tanks in different rate. That is, from every tank the fuel is dropped with some specific rate qi, (i =1,2,3,…. I). The
fuel may arrive from the central buffer, or from other servers. The fluid from a server may go to any of the tanks of the other servers, or to the output of the system which can be represented by the final product store.
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INTCONTMANSYST 9
q1 q2 q3
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INTCONTMANSYST 10
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INTCONTMANSYST 11
Equations of the Motions
I n p u t
)0()( iii utdtu
O u t p u t
tpty ii )( o r0)( ty i
B u f f e r C o n t e n t)()()( tytutz iii
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INTCONTMANSYST 12
CLB (Clear the Largest Buffer Level) CAF (Clear A Fraction) ETC.
Switching Policies
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INTCONTMANSYST 13
Stability of Switching Policies
,)]([sup)]()([sup iit
iit
Mtztytu
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INTCONTMANSYST 14
2. The Scheduling Problem for FMS
One has: n jobs (J1, J2,..., Jn), to be processed on m machines (M1, M2,..., Mm). On machines we understand an equivalent group of machines, which can be in practice one machine, too. Sometimes instead of word machine we use server. For all of the jobs the number of parts ni the due date ddi , the release date ri (ri- is the time at which Ji becomes available for processing) is given. Tsch is the scheduling period.
The CAPP subsystem determines for every job Ji (a job Ji is ni parts of type i ) the operation sequence oij ,where j=1,2,..., jl (jl- is the number of operation sequences), oij –is an integer showing in which machine the given operation is performed. Very important information are the processing times qij . The processing times are determined by the operation planning subsystem of CAPP including the manufacturing data determination section. We note that the engineering database for scheduling is generated from CAPP results. But, it needs proper modification. In engineering database for scheduling it is convenient to apply , where is the number of machines in the equivalent group.
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INTCONTMANSYST 15
The GANTT diagrams
ti1
tilast Tsch t
tj-1
t
tjτi(j-1)
τij
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INTCONTMANSYST 16
Determination of Demand Rates
iup
h
i
j
iilos
i d
h
n
ij
ddT
n
tMaxMaxMin )(,1
)(ch
Tsch0
thh-th server
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INTCONTMANSYST 17
t
demand rate di
il
Part demand ui
i2i1
3. Determination of Demand Rates at Hybrid Dynamical Approach
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INTCONTMANSYST 18
4. The Controlled Buffer Technique
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INTCONTMANSYST 19
Application of Auxiliary Buffer to Supply the Parts According to the Demand Rate
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INTCONTMANSYST 20
Conclusions
The idea to use switching server (hybrid dynamical) approach to the solution of FMS Scheduling problems opens high horizons. Realizing overlapping production, this approach may give significant economical effects. Determining the demand rates as proposed in the present material, or in more detail (and for multiple-machine case) in
Somló [4], simulation studies (based on LabVIEW, Taylor, Simple++ or others) are possible to clear the real nature of the processes.
Somlo. J., Hybrid Dynamical Approach Makes FMS Scheduling More Effective - Periodica Politechnika Budapest University of Technology and Economics, 2001
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INTCONTMANSYST 21
Simulation Based Investigation
By Alexander ANUFRIEV
Ph.D. studentBudapest University of Technology and Economics,
e-mail: [email protected]
Demonstration of Hybrid Dynamical Approach
for FMS Scheduling
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INTCONTMANSYST 22
Warning!Warning!
You should install the AVI codec to view following slides correctly.
Install Now
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INTCONTMANSYST 23
• The simulation model is developed in Taylor ED discrete event simulation software.
Introduction
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INTCONTMANSYST 24
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INTCONTMANSYST 25
Work with Model
Simulation system gives possibility to change parameters before and under the simulation run.
Parameters: Demand Rate Set up time of Server Cycle time of Server Number of Parts for every Part Type Maximum Capacity of Buffers
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INTCONTMANSYST 26
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INTCONTMANSYST 27
Excel Connection
The system writes down the History of the production process to Excel.
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INTCONTMANSYST 28
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INTCONTMANSYST 29
Another simulation system was developed by Koncz Tomas.
(Diploma work at BUTE)
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INTCONTMANSYST 30
Optimal Robot Motion Planning
(Research also supported by OTKA T029072)
By Prof. János Somló
and Ph.D. students: Alexey Sokolov
Zoltán Nagy
Vladimir Lukanyin
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INTCONTMANSYST 31
Introduction
For practical application of robots the motion planning has key role. The optimization of robot motion has special importance for FMS.
New research results were obtained in the field of: time-optimal, process-optimal, force effective, optimal trapezoidal motion planning. In the following demonstration the results are given.
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INTCONTMANSYST 32
Content
1. Parametric Method for Path Planning
2. Trajectory Planning Methods
3. Intelligent, 5-th Generation, LabVIEW Based Robot Control Device
4. The AUTRAP (Automatic Trajectory Planning) System
5. Conclusion
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INTCONTMANSYST 33
1. Parametric Method for Path Planning
Literature survey:
Shin K.G., McKay N.D. (1985), “Minimum-time control of robotics manipulators with geometric path constraints” IEEE of Automatic Control, AC-30, 6, pp. 531-541.
J. Somló - B. Lantos - P.T. Cat (1997), “Advanced Robot Control” pp. 430, Akadémiai Kiadó, Budapest.
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INTCONTMANSYST 34
ni
hx
hx
ii
,...2,1
),(
)(
Direct geometry
Initialpoint
X
B0B
run out
Y
Z
Xn
YnZn
λCruising part
Finalpoint
Transient part
A
A0
B0
B
A0A
run in
v
Inverse geometry
mi
fqq
hq
iii
,...2,1
)()(
)(1
Parametric Method for Path Planning
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INTCONTMANSYST 35
Differential Equation of the Motion From Euler-Lagrange Equation
mj
QQhvgad
dt
da
dt
dv
jextjjjj
,...2,1
;),()()(
;
;
.2
2
2
Parametric Method for Path Planning
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INTCONTMANSYST 36
2. Trajectory Planning Methods2. Trajectory Planning Methods
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INTCONTMANSYST 37
Trajectory Planning Trajectory Planning MethodsMethods
2.1 Time-Optimal Cruising 2.2 Optimal Robotized
Manufacturing 2.3 Force Effective Trajectory
Planning 2.4 Optimal Trapezoidal Velocity
Profile
Content
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INTCONTMANSYST 38
2.2 Optimal Robotized Manufacturing
J. Somlo (1997), “Robotized Manufacturing Process Optimisation”, RAAD’97 Conference, Cassino, Italy, June 26-28, 1997
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INTCONTMANSYST 39
Optimal Robotized Manufacturing
u [m/min]velocity
milling tool
s [mm/rot] feed
A
B
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INTCONTMANSYST 40
Mathematical Model for Optimization
1.) System of Constraints
2.) Performance Index (cost)
3.) Tool Life Equation
},{ ..max proptiiopt vvMinv
.. proptvis obtained
Optimal Robotized Manufacturing
The extremal Tool Life Point Conception
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INTCONTMANSYST 41
Optimal Robotized Manufacturing
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INTCONTMANSYST 42
2.3 Constant Kinetic Energy or 2.3 Constant Kinetic Energy or Force Effective MotionForce Effective Motion
Somló J., Loginov A. (1997), “Energetically optimal cruising motion of robots”, 1997 IEEE International Conference on Intelligent Engineering Systems INES’97, Budapest, September 15-17
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INTCONTMANSYST 43
222
21
22221
211
212
222
211
211
)(2
12
1
qmqqmIIrm
qIVmqIVmT
Kinetic Energy
Force Effective Motion
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INTCONTMANSYST 44
Force Effective Motion
2q12qq
V
tg
tg
122
12
2
qqq
q
)]([
2)(
tg2
1
21
21
2222
22221
211
IS
Tq
qqmqmIIrmT
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INTCONTMANSYST 45
Constant Kinetic Energy General Case (Podurajev J., Somlo J. (1993), “ A New Approach to the Contour Following Problems in Robot Control (Dynamic
Model in Riemann Space) )”, Mechatronics (GB) Vol. 3. N2.)
- the momentary power developed by the drives; - the power given by the end-effector to the object of the work – the overall kinetic energy
texNtmN
dtdT
tmN
texN
T
Force Effective Motion
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INTCONTMANSYST 46
Dynamic model in the Riemann space
11
2/11
2
JIJH
kgSHds
dlR
TS
RFS
T
T
NV
Force Effective Motion
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Constant overall kinetic energy
constSS 0
The constant kinetic energy motion of given position and velocity provides the highest external force
Force Effective Motion
0)( SR
SR
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2.4 Optimal Trapezoidal 2.4 Optimal Trapezoidal Velocity ProfileVelocity Profile
J. Somló - B. Lantos - P.T. Cat (1997), “Advanced Robot Control”, pp. 430, Akadémiai Kiadó, Budapest
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Optimal Trapezoidal Velocity Profile
• Time-Optimal motion has been determined • Trajectory is non-special
Motion with a constant acceleration (deceleration) on the transient part
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START CR STOP
Sa Ea
ES max
crv
Optimal Trapezoidal Velocity Profile
a) )(sa touches )( START at 0 point
b) )(sa goes through the point where)( START and )( CR crosses,and
c) )(sa touches )( START between the above two cases.
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3. . Intelligent, 5-th Generation, Intelligent, 5-th Generation, LabVIEW Based Robot Control DeviceLabVIEW Based Robot Control Device
Personal computer: Pentium II 400 MHz
Control servo drive cards PCL-832 „ADVANTECH”
Multifunctional card ACL-8112PG
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Main features of control servo Main features of control servo card PCL-832card PCL-832
Independent 3-axis servo control Proportional control Built-in F/V converter Servo update time 2ms..2s
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PCL-832PCL-832 Operational principlesOperational principles
DDA PULSEBUFFER
buffer
DDAGENERATOR
CONTROLLOGIC
SUMMINGCIRCUIT
GAIN &OFFSETCIRCUIT
GAINBUFFER
ERRORCOUNTER
12-BITDAC
SERVOMOTORDRIVER
MOTORVELOCITY
BLOCKF/V
CONVERTER
ENCODER FEEDBACK
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Multifunctional cardMultifunctional cardACL-8112PGACL-8112PG
ADC, 16 channels – potentiometer sensors voltage (manipulator
calibration)– voltage current sensor– information about velocity
Programming timer– measure of time system
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DrivesDrives
Power supply Amplifiers 12A8
„ADVANCED MOTION”– PWM– information about
current
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IndustrialIndustrial robot PUMA 560 robot PUMA 560
6 joints Drives DC-motors Position sensors
– encoders (motion control)
– potentiometer sensors (manipulator calibration)
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The main characteristics of this The main characteristics of this control device:control device:
Flexibility Cheap Great possibilities for system monitoring Software module structure Nevertheless, sophisticated
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4. The AUTRAP (Automatic 4. The AUTRAP (Automatic Trajectory Planning) System Trajectory Planning) System The developed LabVIEW based robot control system make possible to realize automatic robot trajectory planning. Recently, we develop such a system named AUTRAP. The AUTRAP system realizes all of the 4 methods for trajectory planning as described in the present paper. The different methods are activated as the user gives one of the commands: TIME-OPT, PROCESS-OPT, FORCE-EF, OPT-TRAP. Override facility is provided for all of the 4 methods. It proportionally decreases the velocities as the override value is given by the user.
Parametric Method for Path Planning
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ConclusionsConclusions
Earlier, almost exclusively, constant velocity or trapezoidal velocity profiles were used in robot motion planning. Recently, as it was outlined in the paper, at least 4 different, rather sophisticated approaches are possible for this problem. The experimental LabVIEW based system developed by us, which uses open system architecture principles, make possible to realize sophisticated control laws. The AUTRAP system, which is under development, implements the outlined methods in an automatic way.