Post on 07-Jun-2020
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CHAPTER 1
INTRODUCTION
1.1 GENERAL
All industrial, commercial and other units rely on electrical
motors for various applications. According to the research by the Electric
Power Research Institute (EPRI), motors account for 51% of the world’s total
energy consumption. The energy consumed by other sectors is comparatively
lower. Lighting, for example, accounts for 19%, heating and cooling system
16% and information technology 14%. Clearly, there is necessity for an
efficient and robust controller for motor control which will lead to saving in
energy.
Electric motors influence almost every aspect of modern living.
Refrigerators, vacuum cleaners, elevators, air conditioners, Washing machine,
fans, computer hard disk drives and industrial processes use electric motors.
In fact, motors consume the most of the energy, no matter what the scenario
is, residential, industrial or commercial application. The energy efficiency of
a motor depends on the type of the motor. Some are built to be more energy
efficient while some are not. Also recent rapid proliferation of motor drives in
the automobile industry with the new hybrid technology has created a great
demand for highly efficient variable speed motor drives.
In many adjustable speed drives, the demand is for precise and
continuous control of speed with long-term stability, good transient
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performance and better efficiency. Conventional DC motors are highly
efficient, simple in construction and have linear torque-speed characteristic.
Conventional DC motors satisfy some of these requirements, however due to
the presence of commutator and brushes, a DC motor requires periodic
maintenance and replacement of brushes. This limits the role of DC motors in
commercial applications. In addition, the ratio of delivered torque to the size
of the motor is poor, restricting its usage in applications where space and
weight are crucial factors- especially in electric vehicles and aerospace
applications. Hence, the Brushless DC (BLDC) motors have evolved as a
better alternative to the conventional DC motors.
1.2 PERMANENT MAGNET MOTOR DRIVES
Improvements in Permanent Magnet materials and power electronic
devices have resulted in reliable and cost effective Permanent Magnet motor
drives for many applications. There are two types of Permanent magnet
motors based on their back emf waveforms: Permanent Magnet Synchronous
Motor (PMSM) and Permanent Magnet Brushless DC (PMBLDC) motor
type. A PMSM drive requires, continuous and accurate detection of rotor
position information, making the system complex. On the contrary, a
PMBLDC motor drive requires rotor position information only at six instants,
making the commutation process easier and simple.
The PMBLDC motor drives are appealing candidates for many high
performance applications because of their attractive characteristics. The
important features are better speed-torque characteristics, good dynamic
response, high power density, reasonable torque to inertia ratio, better
efficiency, robustness, long operating life, noise less operation and reliability.
From the modelling perspective, a BLDC motor looks exactly
similar to a DC motor, possessing a linear relationship between current and
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torque, voltage and speed. It has an electronically controlled commutation
system instead of mechanical commutation as in the case of conventional
brushed DC motors.
1.2.1 Details of PMBLDC Motor
A PMBLDC motor with the trapezoidal back emf waveform and
quasi square wave current is referred as BLDC motor in this thesis. In the
BLDC motors, the back emf induced, has trapezoidal waveform and the stator
must be supplied with a quasi square wave current waveform, whereas in the
sinusoidal motors, the induced emf is sinusoidal with the current fed to the
stator being a sinusoidal waveform. Figure 1.1 shows the ideal back emf
waveforms of the BLDC motor for the trapezoidal and sinusoidal types.
Figure 1.1 Ideal back emf waveforms of the BLDC motor for the
trapezoidal and sinusoidal types
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1.2.2 Operation of BLDC Motors
A three phase BLDC motor is normally operated on a two phase
on-mode, i.e. the two stator phases that produces the highest torque are
energized leaving the third phase open. The commutation instants are
determined by the rotor position sensors. Based on the rotor position signal,
the stator phase windings are energized. There are two types of control
techniques, namely, sensor based and sensorless based control. The BLDC
motor control based on sensors has many disadvantages. Mechanical sensors
increase the inertia of the rotor shaft and they require additional space. In
order to overcome these issues a sensorless technique is preferred.
1.3 ARTIFICIAL INTELLIGENCE TECHNIQUES IN
ELECTRIC DRIVE APPLICATIONS
Advances in AI techniques like Neural Networks, Fuzzy Logic,
Genetic Algorithm, and Particle Swarm optimization techniques have made
tremendous impact on the engineering applications. Recently, AI techniques
are making a great impact in electrical engineering, particularly in the area of
Power Electronics application in motor drives. AI technique is basically a
computer simulation of human thinking usually referred as computational
intelligence. They are used in many interesting areas of power electronics and
motor drives applications.
AI techniques are basically classified into four main categories.
They are the Expert system, Fuzzy Logic (FL) system, Artificial Neural
Network (ANN), and Genetic Algorithm.
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Expert Systems
Expert system is basically an “intelligent” computer program based
on Boolean logic that is designed to impart the expertise of a human being in
certain domains to solve complex problems. Expert system is referred as
“hard” or precise computing, whereas FL, ANN and GA are referred as
“soft” or approximate computing. The Software for the knowledge base or the
rule base is well organized that, it has easy learning, altering and updating
capabilities. Expert systems are applied in the tuning of controller parameters,
fault diagnostics, and automated testing of drives.
Fuzzy Logic Systems
Fuzzy logic control is another class of AI technique, but its history
is more recent than the evolutionary systems. A FLC is a heuristic approach
that embeds the knowledge and key elements of human thinking in the design
of nonlinear systems. In the fuzzy set theory, a particular object has a degree
of membership in a given set that may be anywhere in the range of 0 to 1.
Each fuzzy set is defined by a linguistic variable, which is again defined by a
multi valued membership function. The FL controllers are based on fuzzy set
theory. Therefore, the boundaries of fuzzy sets are, vague and ambiguous
making themselves useful for approximation models.
Artificial Neural Networks
Artificial neural networks have found wide spread use in function
approximation. The ANN techniques do not require a mathematical model.
They give an improved performance when tuned properly and requires less
tuning effort than conventional controllers. They have a simple design
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procedure which includes the data from a real system even in the absence of
necessary expert knowledge. ANN is used in power electronics and motor
drive applications to estimate the rotor speed, flux, resistance and position of
the rotor.
Genetic Algorithm
Genetic algorithms are a part of evolutionary computation
algorithm, which use a probabilistic method of solving optimization problems
and search problems. In GA based solutions, an initial population is assumed
and then the optimization is reached after several generations that involve
reproduction, crossover and mutation operations. Genetic algorithms are
applied in many situations like solving nonlinear system transcendental
equations and in the optimization of FLC. Genetic algorithms are derivative
free and they are not affected by the local minima.
Most of the consumer products such as washing machine, auto
focus cameras and air conditioners involve a FL control technique. The
application of the Evolutionary system and the GA in the field of Power
Electronics and motor drives are limited when compared with FLC and ANN.
1.4 LITERATURE SURVERY
This section deals with a brief review of recent developments in the
field of BLDC motors, sensorless control techniques and AI techniques for
the speed control of BLDC motor drives.
1.4.1 BLDC Motor Modelling and Sensorless Control Techniques
Bimal Bose (2006) has discussed the entire issues in Power
Electronics and motor drives, covering almost all the types of control
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techniques applicable to the Induction motor, PMBLDC motor, PMSM and
Switched reluctance motor drives through simulation and experimental
results.
Pillay and Krishnan (1988) modeled a Permanent Magnet motor
drive and studied the system responses under steady state and dynamic
conditions. A later work by the same authors (1989) was on the exclusive
modelling of BLDC motor drives and its analysis. They (1991) have also
analyzed the operating characteristics of the PM sinusoidal and PM
trapezoidal motor for servo applications. Gencer et al (2006) also have
modeled a three phase brushless DC motor using Matlab/ Simulink to predict
the accurate system behavior.
An advanced simulation model of a sensorless controlled BLDC
motor drive using Matlab/Simulink was developed by Byoung et al. (2003).
With the developed simulation model they have analyzed the steady state and
dynamic characteristics of the motor drive. The advantage of this sensorless
model is that, it is free from phase delay circuit and it is independent of the
machine parameters. A similar work was also carried out by Somanathan et al
(2006) for the PMBLDC motor drives. The sensorless control technique was
based on the detection of the back emf zero crossing without using the motor
neutral point.
Sensorless operation of the BLDC motor drive has been a
research topic for more than two decades. The back emf sensing and the
detection of the freewheeling diode conduction are the main categories of the
sensorless control techniques.
Iizuka et al (1985) originally proposed a simple motional emf based
scheme for the sensorless operation of trapezoidal machines. The zero
crossing events of the motional emf of the open phase were used in sensing
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the speed and position of the rotor. However, the back emf zero crossing
events did not match with the actual position of the rotor. Therefore, the
signals were phase shifted by 90 electrical degrees. Besides, it cannot be used
at low speed operation and requires heavy filtering circuit that affects the
dynamic characteristics of the drive.
Different commutation strategies for brushless DC motors have
been studied by Berendsen et al (1993). The influence of torque on the
different control strategies were discussed with the simulation and
experimental results. A low cost sensorless control technique for brushless
DC motor drives using a frequency independent phase shifter was studied by
Jung and Ha (2000). The problems of tackling the phase shifting have been
discussed.
Toliyat H et al (2002) discussed a position sensing technique applicable to
surface mounted PM machine with insignificant saliency. They have
developed a tapped machine winding to cancel the third harmonic component
and the resistive voltage drop. This led to a considerable reduction in errors
caused due to the change in resistance because of the heating effect.
Kuang et al. (2003) proposed a modified back emf sensing scheme to sense
the position of the rotor without using hall sensors. They have introduced a
digital phase compensation circuit to reduce the commutation error caused
due to low pass filtering and the non ideal effects of back emf waveform.
Paul et al (2006) reviewed the sensorless techniques of the BLDC
motor. They have discussed the merits, demerits and limitations of the various
sensorless techniques available in the literature from 1974 to 2005.
Shao et al (2003) presented a novel microcontroller based
sensorless control technique for the BLDC motor drive. In this method, the
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true back emf signals were extracted directly from each phase without using
neutral point. The advantage of this control technique is that it does not
require filters and is insensitive to switching noise. The proposed method was
successfully tested on an automotive fuel pump.
A new phase delay free method to detect the true back emf zero
crossing instants for the sensorless operation of spindle motors has been reported
by Jiang et al. (2004). They have used digital filters to identify the true zero
crossing points. This method is independent of motor parameters and suitable
for high speed operation.
Cheng et al (2005) discussed the steps involved in the design and
implementation of a sensorless controlled BLDC motor drive. The advantage
of this method is that it is insensitive to common mode noise, and does not
require either phase shifting or neutral point voltage.
Jang et al (2005) adopted a bipolar starting and unipolar running
method for a hard disk drive spindle motor operated at high speed with large
starting torque. A novel inverter circuit was developed such that, high starting
torque was achieved with the bipolar starting and high speed was maintained
with the unipolar driving.
A novel position sensorless control technique based on coordinate
transformation was proposed by Yuanyuan et al. (2010). They have used
back emf zero crossing technique for the sensorless operation of the BLDC
motor drive. The rotor position errors are compensated by controlling the
difference in phase angle.
Abolfzal et al (2008) proposed a novel cost-effective sensorless
control technique for a three phase BLDC motor drive. They have used only
four switches instead of six switches to control a three phase BLDC motor
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drive. The sensorless technique used in this paper was based on the detection
of zero crossing point (ZCP) derived from the filtered terminal voltages.
Alternate methods of position sensing schemes based on
observers, inductance and flux linkage variations are also known for more
than a decade. A mathematical model of the motor drive and converter were
used in the process of estimating the output of the system. The estimated
outputs were compared with the measured outputs to generate an error signal.
This error signal is fed back to the motor model to correct the estimates.
Several authors have highlighted these features in their research work.
Kim et al (2008) proposed a new approach to the sensorless control
of the BLDC motors to alleviate the drawbacks in the back emf based
sensorless techniques during starting. They have modeled the trapezoidal back
emf waveform as an input observer to estimate the line to line back emf which
is used to extract the position of the rotor. This technique gives better results
at low speeds than at high speed range.
The sensorless control technique for the BLDC motor drive based
on the zero crossing detection of back emf from the line voltage difference
was presented by Damodharan et al. (2010).
An alternate method of sensorless position detection technique for
the BLDC motor drives was reported by Kim et al (2004). The commutation
instants of the drive from near zero speed to high speed were estimated by
using a speed independent position function. This method does not require an
external hardware to sense the terminal voltage and it is independent of the
measured back emf.
Su et al. (2004) presented a low cost sensorless control technique
for BLDC motor drives. They have indirectly extracted the rotor position
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information from the back emf waveform. But, it requires a low pass or a
band pass filters to retrieve the position information from the measured back
emf.
Matsui et al (1992, 1996) proposed a closed loop observer based
position sensing scheme for a PM machine with significant rotor saliency.
Two alternative models namely, the voltage and current models were used in
the position estimation technique. The limitation of this method is that it has
not addressed the problems associated with the sensorless starting and it also
requires a separate starting arrangement.
Ertugrul et al (1994) and French et al (1996) proposed a control
strategy for estimating the position of the rotor. They have used the varying
flux linkage to estimate the position of the rotor.
A novel speed control technique for the PM sine wave, and square
wave motors was presented by Chan et al (1995). The advantages of the speed
control technique are: it can be applied directly to any brushless dc motor
drive without any co-ordinate transformation and it can be used for a wide
range of speed control.
An alternate method of sensing the position of the rotor by
monitoring the rate of change of winding current was proposed by Nakashima
et al (2000). The presented technique is applicable only to the surface
mounted PMSM drives.
Bhim Singh et al (2003) presented a gain scheduling scheme for the
PI speed controller used in the PMBLDC motor drives. The PI gains were
allowed to vary within a predetermined range to eliminate the problems
associated with the PI controller.
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Sensorless operation of the BLDC motor drive using the back emf
information has been widely used in low cost applications like hard disk
drives. But, when the motor is at stand still or at low speed, it is very difficult
to get the position information from the back emf. Therefore, a special
starting arrangement was required to overcome this difficulty.
A Sensorless rotor position estimation technique implemented to
the interior PM motor from its initial states was discussed by Ha JI et al
(2003). In this paper, authors have discussed the various control techniques
regarding the torque, speed and position control method at stand still and at
low speed for the interior permanent magnet motor drive system. In order to
estimate the initial position of the rotor, a high frequency signal was injected
into the estimation circuit to detect the initial position of the rotor.
Tursini et al (2003) reported on the application of inductance
methods to address the problem of sensorless starting. However, the sensed
position was valid only for a period of 180 degree electrical, whereas, it is
problematic for 360 degree electrical.
Ying et al (2003) proposed novel DSP-based indirect rotor position
estimation for the permanent magnet AC motors without using the rotor
saliency. The problems associated with the flux integrator drift have been
addressed by considering the relationship between incremental changes in
flux linkage with rotor position to improve the response of speed.
Cho et al (2004) presented a model based approach to sense the
back emf in the surface mounted PM machine used for a direct drive washing
machine. In this method, the temperature of the washing machine was
estimated using the stator resistance when the rotor speed was zero, and an
appropriate correction was carried out in the model.
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Jang et al (2004) conducted a detailed experimental and finite
element analysis to investigate the impact of injecting a high frequency signal.
A new rotor position detection method for the sensorless control of the
spindle motors in the hard disk drive was presented by Quang J et al (2005).
In this method, the digital filters are used to identify the true and false zero
crossing points of the back emf.
Wook et al (2006) proposed a novel method of detecting the rotor
position at standstill in the BLDC motors used in the Hard Disk Drive. The
back emf based sensorless technique was used in the analysis. They have
proposed a startup method to accelerate the rotor up to a certain speed till it
acquires sufficient back emf. It is demonstrated experimentally that the
proposed method showed a stable operation during the entire range of
operation even in the presence of several mechanical disturbances.
Kim et al (2007) reviewed the position sensorless control
techniques of the BLDC motor and generators. The authors covered a wide
range of topics related to the control of BLDC motor, their limitations,
advantages and its future scope. Rodriguez and Emadi (2007) presented a
novel digital control for brushless DC motor drives using conduction angle
control and current mode control. The experimental demonstration was
carried out using a real time interfacing system.
Dong et al (2008) analyzed the characteristics of terminal voltage
which is used for detecting the rotor position. A simulation model of the
interior PMBLDC motor was developed to analyze the advancement in the
ZCP due to the variation of inductance. They have obtained a relationship
between the abnormal currents and the position errors. This relationship has
been included in the simulation model to minimize the position errors.
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Lee et al (2008) proposed a novel position sensorless starting
method for the surface mounted permanent magnet BLDC motors used in the
low cost applications. In order to have current lesser than the demagnetization
current and also to limit the motor current during the starting period and the
commutation was carried out based on the measured phase current.
The BLDC motor drives with four switches have an advantage that
there is a considerable amount of reduction in conduction losses and reduction
in switches and freewheeling diode current. Hence, they are used in the low
cost applications.
Lee J H et al (2000) and Lee K (2003) have developed a BLDC
motor drive using a four switch, three phase inverter topology. They have
used position transducers to sense the commutation instants of the drive. But,
the sensorless technique based on the back emf zero cross detection requires
an unexcited phase to detect the zero crossing instants. To overcome this
drawback, Lin et al (2008) proposed a novel FPGA based position sensorless
control for a three phase BLDC motor drive using four switches. In this
scheme, a novel asymmetrical PWM technique with six commutation models
was used. The main drawback of this four switch topology is that it produces
undesirable torque ripple.
The sensorless techniques based on the back emf detection have a
drawback that, either it can be used in the low speed range or in the high
speed range. Yen Shen et al (2008) proposed a novel technique for the BLDC
motor drive which can be used for both low duty-ratio and high duty-ratio
control. An FPGA based novel digital PWM control scheme for the BLDC
motor was proposed by Anand et al (2009). Bhim Singh et al (2009) studied
the different sensorless control techniques for the BLDC motor drives.
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An advanced double loop control strategy based on the four switch
topology applied to the three phase BLDC motor was proposed by Xia et al
(2009). A single neuron was used in the adaptive Proportional–Integral speed
control algorithm to improve the performance.
A novel starting method of sensorless control technique for the
BLDC motor used in the electric vehicle was presented by Mei Ying et al
(2010). The principle applied in the estimation of the position signal was
based on the variation of current response caused by the magnetic saturation
of the stator core, when the current flows along the magnetic axis. A
sensorless control technique suitable for the BLDC motor drives used in the
electric propulsion of small ships and underwater vehicles was presented by
Min- Fu et al. (2010). In this technique, a low pass filter with two cut off
frequencies were used to regulate the speed from a low range to a high range.
Yen et al (2011) presented a unified approach for detecting the back
emf zero crossing points in the sensorless operation of BLDC motor. They
have also investigated the influence of various PWM techniques for the
estimation of position in the sensorless approach.
1.4.2 Artificial Intelligence Techniques in Electric Drives and Control
In recent times, AI techniques are making a great impact in the
motor drives and control industry. Computers can be made to think like
humans with the help of AI which has the capability to mimic human
intelligence. This capability of AI makes it suitable for many applications in
the area of power electronics and motor drives applications.
Lee et al (1990) carried out a study on FLC schemes. The general
methodology for constructing a FL controller, assessment of its performance
and its future scope are discussed in this paper. Kim et al (1994) proposed a
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Fuzzy logic based pre-compensation scheme to complement the PID
controllers. In their research work, an FLC was integrated with the PID
controller to improve its performance. The proposed technique was also
verified by implementing it on a DC servo motor drive.
Homaifar et al (1995) examined the applicability of the GA to the
simultaneous design of membership functions and rule sets of the FL
controller. Sousa et al (1995) discussed the applications of FL controller and
reviewed the FL Control theory in power electronics and drives. A discussion
on the design and implementation aspects of the FL controller was presented,
and the interaction of neural networks and FL control techniques are also
considered in their research work.
Tipuswan et al (1999) proposed an alternative method to implement
the FL speed controller to a DC motor using a microcontroller. The controller
has the advantage of high performance with compact size and low cost.
Modelling and controlling of non linear system based on the evolutionary
design procedure was presented by Kang et al (2000). They have explained
the design procedure with four different numerical examples.
Ahmed et al (2002) adopted an online training algorithm to the
BLDC motor drive using FL and NN. The proposed controller integrates the
ideas of the FLC and NN structure into an intelligent control system. An
ANN-based structure was introduced for the FL control system.
The hardware implementation of the microprocessor based fuzzy
logic controller is carried out by Rubaai et al (2002). The results discussed
indicate that it possess excellent tracking performance for both speed and
position trajectories. After studying the problems in the design and tuning of
fuzzy controller, Changliang (2004) proposed an auto tuning method based on
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GA. The GA based fuzzy controller was applied to the speed control of the
BLDC motor.
Kim et al (2005) investigated the nature of torque in a sensorless
controlled BLDC motor as a function of commutation delay. A commutation
unbalance compensator and maximum torque controller were introduced in
the drive for advancing the commutation and maximizing the torque
generation.
Timothy J Ross (2005) published a text book titled “Fuzzy logic
with engineering applications”. The entire concepts of the FLC with a few
industrial applications were discussed in this book. Nasir Uddin et al (2005)
presented a novel speed control scheme using a GA based FL controller
(GFLC) for the interior PMSM drive. This controller was designed and
implemented in the real time using a digital signal processor board. The
parameters of the FL controller were tuned by GA.
To improve the performance of conventional sensorless control
technique, Park et al (2006), proposed a robust fuzzy back emf observer for
the continuous estimation of speed. The robustness of the proposed algorithm
was proved through the simulation studies and is compared with other
sensorless control methods. A PID type FL based on NN controller was
designed by Muammer et al (2007). In this paper, the steady state and
dynamic behavior of the motor drive were analyzed.
Ahmed et al (2008) discussed the findings of the design and
experimental verifications carried out on a hybrid FL control system used in
the BLDC motor drive. The laboratory implementation of this controller was
based on a linguistic fuzzy controller.
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A DSP based laboratory implementation of hybrid fuzzy PID
controller using the GA based optimization for the high performance motor
drives was reported by Ahmed et al (2008). In this work, authors have made a
real-time implementation of the GA based hybrid Fuzzy-PID controller for
the industrial motor drives. The design of Fuzzy-PID controller and its
integration with the conventional PID for the design of the hybrid controller
have been demonstrated. The transient and steady state behavior of the BLDC
motor drive using a multi input FL controller was studied by Bharathi et al
(2008).
Mirtalei et al (2008) presented a novel sensorless control strategy
for the BLDC motor drives using the Neural Network Observer. They have
replaced the FL based observers with the two neural networks to make the
process easier. This method gave an accurate position information for a wide
range of speed.
Amit et al (2009) adopted a novel switching function for the speed
control of PM synchronous motor using a hybrid (Fuzzy + PI) controller. The
switching functions were calculated based on the weights of the controller
output.
Jiao et al (2009) discussed the various problem associated with the
conventional PID controller and proposed a FL controller based on the sliding
mode control for the position sensorless operation of electric vehicle. The
controller developed had a facility to adjust the switching gains of the FLC to
avoid the whippings. Patil et al (2009) discussed the steps involved in the
design and real time implementation of integrated FL controller for the high
speed PMDC motor. They have analyzed the performance of the proposed
controller by testing it with various test signals such as square, triangular,
sinusoidal and step signals.
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Mahesh Kumar et al (2009) proposed a simple speed control
technique for the BLDC motor drives using FL controller. The FL was used
as an observer to estimate the speed of the drive by measuring the stator line
voltage and current. The limitations of the fixed gain and its effect on the
performance of the BLDC motor drive were discussed by Srinivas et al
(2009). To overcome the limitations of the fixed gain, a FL based gain
scheduling scheme was implemented in the Digital Signal Processor.
A literature survey on the various AI techniques for the BLDC
motor drives were presented by Gupta et al (2010). This literature survey acts
as guidelines and quick reference for the researchers and practicing engineers
who are working in the area of the PMBLDC motor drives.
Varatharaju et al (2010) made a Matlab/Simulink model of the
PMBLDC motor drive to study the performance of the drive for the change in
load torque and inertia. The simulation model was not validated either with
analytical model or with the experimental setup. The simulation model was
not subjected to different load perturbation. Therefore, this model cannot be
used to predict the actual performance of the drive.
Malhotra et al (2010) presented a review on the technological innovations
in soft computing techniques to the new level of applications. The
performance of different process controllers has been analyzed and compared
with the FL controller.
Shun-Chung et al. (2011) proposed a modified PI like FLC with
self tuning mechanism. The proposed technique was based on the
redevelopment of control rule base to reduce the number of rules and
membership functions, thereby reducing the complexity of the controller.
Rajani and Nikhil (2011) proposed a robust self tuning scheme for FLC. The
output scaling factor was adjusted online by fuzzy rules according to the
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current trend of the controlled process. Authors have proved that, the
complexity in the control and cost in implementing the hardware were
considerably reduced.
Varatharaju et al (2011) overcame the drawbacks of the FLC and
ANN based controller by using an Adaptive Neuro-Fuzzy Inference System
(ANFIS). A Comparison between FL controller and Adaptive FLC in the
speed control of the BLDC motor was presented by Alizadeh (2011). Nagaraj
(2011) discussed the different methods of tuning a PID controller. The
methodology, efficiency and the performance of the AI speed controller are
compared with the traditional PID controllers. Subbarami Reddy et al (2011)
compared the performance of the PID and the Hybrid controller by
considering the various performance measures such as the rise time, settling
time, steady state error, peak overshoot, the Integral of the Absolute value of
the Error and the Integral of the Time-weighted Squared Error.
Siong et al (2011) developed a fuzzy logic model to carry out the
simulation and experimental studies on a PMBLDC motor drives. The
dynamic characteristics of the drive such as, speed, torque, current and
voltage of the inverter components are observed and analyzed using the
developed MATLAB model.
Nikhil et al (2011) presented the steps involved in the
implementation of DSP based FLC scheme for a BLDC motor. A three phase
four switch topology was used in the analysis .
Kandiban et al (2012) proposed an adaptive Fuzzy-PID controller
for the speed control of BLDC motor dive. The performance comparison of
the PID controller, Fuzzy Logic-PID controller and an adaptive Fuzzy PID
controller were discussed in their research work. The difficulty in tuning the
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parameters of the PID controller was overcome with the adaptive fuzzy logic
controller.
The exhaustive literature review conducted on the BLDC motor
drive has revealed that, the research work carried out on the BLDC motor
drive shows the influence of the technical developments in Power electronics,
microelectronics and control system on the application of AI. Most of the
developments in these fields were in the direction to reduce the hardware by
introducing high-speed digital signal processors and to reduce the sensing
requirements for the drive system.
In the literature, there are several simulation models available for
BLDC motor drives. These models employ state-space equations, Fourier
series or d-q axis model. Even though these models have made a great
contribution in the BLDC motor drives, there is no comprehensive model for
the analysis of motor used in sensorless operation. The motor models
available in the literature discuss the mathematical analysis of the BLDC
motor and a computer simulation of such models. The verification and
validation of such models were not discussed in detail. Moreover, the
assumptions made during the mathematical modelling of the motor control
system limits the feasibility of such models in the prototype development. In
the model based design process, these affect the performance of the motor in
terms of accuracy, stability and robustness. Hence, there is a need for a
simplified and real time model which can be validated, corrected and can be
extended directly to the prototype development.
This thesis, presents a review of the most commonly used
sensorless control techniques with a comparison to determine which one is the
best candidate for the industrial applications. The literature survey also
revealed that, the sensorless method based on back emf estimation has been
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found to be more promising. Hence, the back emf based sensorless technique
is further investigated in detail with the AI controller.
Literature survey also reveals that even though AI controllers were
analyzed and investigated over a decade, the successful implementation of
this controller to the sensorless controlled BLDC motor drives with
trapezoidal back emf is very limited. Even though neural network and FL
controllers were used as observers for sensorless position estimators, the real
time/experimental validation of these techniques is very limited.
In this thesis, a study on the performance enhancement of BLDC
motor drives operated in a sensorless mode is considered. To show the
suitability of the proposed technique on a sensorless environment, Fuzzy logic
controller and Genetic Algorithm were analyzed on a BLDC motor without
sensors.
1.5 REAL TIME VALIDATION AND DSPACE
The recent development in the software technology has made the
design and development process simple, cost effective and less time
consuming. The new technology has reduced the complexity in the
verification, validation and testing process, making the entire process simpler.
When changes occur in the system, instead of redesigning the whole system,
corrections can be made then and there, therefore reducing the time taken for
computation. Even though there are several simulation models in the
literature, the direct extension of such models in the prototype development is
very limited. The dynamics assumed during the modelling will not be the
same as observed in the real time system.
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A simulation model in a Matlab/Simulink will automatically
generate the source code with the help of Real Time Workspace (RTW). The
generated code is tested and compared with the behavior of the mathematical
model and it is validated with a real time simulator. The generated source
code is uploaded to the appropriate hardware in loop simulator with the
necessary peripherals. The generated code is linked with the real system
through Real Time Interface (RTI) and Real Time Embedded coder. This
embedded coder again generates a source code which is optimized to mimic
the real system.
1.5.1 Real Time Interface (RTI)
The Real Time Interface is the link between DSPACE real time
hardware and the Matlab/Simulink development software. The RTI
automatically runs through all the steps necessary to build the application
such as generating C code and invoking the compiler. It also extends the C
code generated from the real time workspace to the simulation models which
can be implemented easily on the DSPACE real time environment.
1.5.2 DSPACE
DSPACE and Matlab RTW have created a rapid control prototype
environment which has facilitated the design engineers to focus only on the
design rather than on programming. DSPACE approach has the advantage of
directly extending the design skills of engineers from Matlab/ Simulink
environment to the hardware implementation. It is a simple user interface,
which requires only a basic knowledge of Matlab/Simulink for designing the
controller while the experiment is running. The parameters of the controller
and the reference signal can be changed easily with the help of DSPACE.
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The design and implementation phases are carried out using
Matlab/Simulink and the commercial real time hardware DSPACE
DS2202/DS1104/DS2205 and other series of DSP board. The controller block
uses Matlab RTW routine that automatically generates the C code from the
Simulink block. The generated code is used by the DSPACE DSP board for
the real time control. The Simulink and DSPACE together help to validate the
model on a laboratory platform, at a click of mouse button.
1.6 OBJECTIVE OF THE THESIS
This research work deals with various aspects of applying AI
techniques to enhance the performance of the BLDC motor drive. In the
presented work, AI technique is used in the speed control of the BLDC motor
drive operated in the sensorless mode.
The specific objectives are as follows
To develop a simplified simulation model of the BLDC
motor drive with hall sensors and to validate the model with
real time/experimental setup, and extend the simplified
simulation model for the sensorless control of the BLDC
motor drive.
To optimally tune the parameters of PI controller using GA
To design, develop and investigate the influence of fuzzy
logic and the genetic algorithm on the BLDC motor drive.
To design an improved GA based optimal tuning of the
parameters of FLC and to compare the performance with a
modified self tuned FLC.
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The scope of the presented work is given in Figure.1.2.
Figure 1.2 Scope of the present work
Figure 1.2 Scope of the presented work
Performance enhancement of BLDC
motor drives using AI techniques
Simplified modeling and sensorless
speed control of BLDC motor drive
Design of AI controller for the BLDC
motor drive
FL based speed
controller
GA based speed
controller
Identify the suitable controller for the enhancement
of the BLDC motor drive
Compare the speed response of the BLDC motor
drive with AI controllers
GA tuned PI
controller Improved
GA based
FLC
Simple FL
controller
Modified
STFL
controller
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1.7 ORGANIZATION OF THE THESIS
The work reported in the thesis is organized in to seven chapters,
Chapter one presents the problem under investigation, the objective
of the present work, scope, organization of the thesis, a brief review of
literature on the modelling of the BLDC motor drive, sensorless control
techniques of the BLDC motor drive, and AI control techniques for electrical
motor drives.
The description of a simplified simulation model of the BLDC
motor drive with hall sensors is presented in Chapter two. The simulation
model consists of several sub blocks like the hall sensor block, the voltage
and current equations block, which also includes the inverter and switching
logic control. The responses obtained from the BLDC motor drive during
different operating conditions were compared with the ideal waveforms and
also with the waveforms available in the literature. The results of the
simplified simulation model were compared with those obtained from the
developed hardware setup. The simulation and hardware results matched
closely, and hence, this model was further extended to the sensorless control
technique and the design of the AI controller.
The sensorless control, based on the back emf zero cross detection
is a superior and widely used technique by the motor drives industry. Since
this work is focused only on the study of AI controller techniques for the
BLDC motor drives, the sensorless control scheme based on the back emf
zero crossing was used to study the proposed controller.
The simplified simulation model developed for the BLDC motor
drive with hall sensors has been extended, to obtain a sensorless speed control
technique in Chapter three. The developed sensorless model of the BLDC
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motor drive with the PI speed controller was compared with the responses
available in the literature and also with the experimental results. A detailed
analysis carried out on the sensorless controlled BLDC motor under different
operating conditions showed, that the sensorless model can be further used in
the study of the proposed AI controller for BLDC motor drives.
The GA based tuning of the parameters of the PI controller is
discussed in Chapter four. The steps involved in the optimal tuning of the
parameters of the PI controller, the problem formulation and the results
obtained, are also included in this chapter.
A simple Fuzzy logic controller (SFLC) was designed to study the
direct impact of the fuzzy logic in the BLDC motor drive. The results
obtained from the SFLC clearly reveal that it requires an optimal tuning to
improve the performance of the drive. A modified self tuned FLC (MSTFLC)
was proposed in Chapter five to improve the performance of the drive.
The MSTFLC gave a good improvement in the performance of the
drive when compared with the SFLC, but the initial gains of the controller
were obtained by trial and error method. Hence, an Improved GA based
optimal tuning of the parameters of the FLC (IGAFLC) was proposed in
Chapter six, to further improve the performance of the FL controller. The
results obtained from the proposed GA based optimal tuning of parameters of
the FL controller shows an improvement in the performance of the BLDC
motor drive.
Chapter seven gives a summary of the conclusions obtained from
the research work, and suggestions for future work. The references are listed
at the end.