Hybrid Fuzzy PID Control for BLDC Motor

This report delves into the application of hybrid fuzzy PID control for Brushless DC (BLDC) motors. It begins with an introduction to motor control, highlighting the importance of high-performance motors in various industrial applications. The report discusses the limitations of conventional PID controllers, particularly their struggle with non-linearities in DC motors. It then introduces Fuzzy Logic Control (FLC) as an alternative, detailing its advantages such as faster system response and simplicity in design. The challenges of fuzzy techniques, including the difficulty in selecting membership functions and developing fuzzy rules, are also addressed. The report outlines the objectives of the project, which include modeling the BLDC motor, controlling its speed using conventional and fuzzy-PID methods, and analyzing the sensitivity of membership functions. The problem statement emphasizes the need for improved control techniques to overcome the limitations of traditional PID controllers. The report concludes with a list of references used in the study.

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HYBRID FUZZY PID CONTROL FOR BLDC MOTOR

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Contents
HYBRID FUZZY PID CONTROL FOR BLDC MOTOR ................................................4
CHAPTER 1........................................................................................................................4
INTRODUCTION.......................................................................................................................4
BACKGROUND.........................................................................................................................6
FUZZY LOGIC CONTROL.......................................................................................6
ADVANTAGES...........................................................................................................7
CHALLENGES OF FUZZY TECHNIQUES.............................................................8
FUZZY LOGIC APPLICATIONS...............................................................................8
OBJECTIVE................................................................................................................................9
PROBLEM STATEMENT........................................................................................................10
REFERENCES..................................................................................................................12

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HYBRID FUZZY PID CONTROL FOR BLDC MOTOR
CHAPTER 1
INTRODUCTION
Motor stands as a very basic part from very small electronic device to a huge
electronic device and for the larger and high powered electronic devices, high
performance of the motor becomes vital, especially, in the industrial applications,
like electric trains, steel rolling mills and robotics, where the basic functionality of
the device would be majorly dependent on the performance and reliability of the
motor. Eventually, a decent motor drive system with higher performance demand
the vital features, such as load regulating response, command tracking with good
dynamics speed, to achieve the reliable and expected performance, for longer
period of time. DC drives act as the best and ideal solution for these industrial
requirements, for their ease of application, favourable cost, high reliabilities and
flexibilities and so are considered as the backbone of the home appliances, robot
manipulators and industrial applications. The matching in all these applications by
DC drives is because of the requirements of control of position and speed of the
motor. The complexity of the DC drives is lesser than that of the AC drives,
because the characteristic of speed torque of them, is much more than AC drives.

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In addition to that, DC motors have tremendous speed control for deceleration and
acceleration and are available at less expensive cost, for even higher ratings of
horsepower. All it needs is just a single system for conversion of power to DC from
AC. DC drives has become a tradition in the industrial applications and home
appliances, because of the machines that are adjustable with speed and various
features. The logic for the match of the applications is that DC drives allows the
designers and developers of the existing and new electronic applications, for the
precise control of the speed of the motor, for the desired performance. The speed
controllers have been the key to performance, of the motors, because of DC motor
speed control can execute numerous varied tasks, from conventional controller
types and numeric controller types. These speed controllers in application are
usually of FLC (Fuzzy Logic Controller), PID (Proportional Integral Derivative)
and PI (Proportional Integral) and combination of these controllers, such as Fuzzy
Genetics Algorithm, Fuzzy=Neural Networks, Fuzzy Swarm1. The majority of the
control system in the electronic industry in the world is operated by the PID
(Proportional Integral Derivative), over 95% of the industrial process control
applications. The dominated use of the PID in these applications is because of the
unmatchable simplicity, applicability and clear functionality and ease of use,
1 BomedieneAlloua and ABdessalamAbderrahamani, “Neuro-Fuzzy DC Motor
speed Control Using Particle Swarm Optimization,” Leonaro Electronic
Journal of Practices and Technologies ISSN,1583-1078.

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compared to the other controllers2. Hence, the PID controllers provide reliable
performance and robust performance, for the electronic systems, when apt usage of
the parameters and tuning of the PID is set.
However, there are certain major issues associated with the control
algorithm, used conventionally, such as PID, PD, PI, for the DC motors, is non-
linearity effects. The conventional controllers’ performance can be degraded by the
DC motor’s non-linear characteristics, like fictions and saturation, etc.3. It is
generally, difficult find the actual DC motor’s non-linear model and only
approximated values can be obtained for the parameters, from systems
identification.
BACKGROUND
FUZZY LOGIC CONTROL
Fuzzy Logic Control has been introduced and developed in 1973, by L.A.
Zadeh and started its application, in 1974, by Mamdani, as an attempt to control
system, which have difficulty in modelling structurally. And FLC has become one
of the significant fuzzy set theory applications and has been spreading in recent
years, with rapid progress. The progress of the FLC has become fruitful and
extremely active area of research, having numerous industrial applications. Hence,
2 J. Zhang, N. Wang and S. Wang, “A developed method of tuning PID
controllers with fuzzy rules for integrating process, ( 2004) ” Proceedings of the
American Control Conference, Boston, , pp. 1109-1114
3 B.J. Chalmers, “Influence of saturation in brushless permanent magnet
drives”, ( 1992) IEE proc. B, Electr.Power Appl, vol.139, no.1.

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the FLC has been evolved as a complementary and best alternative to the control
strategies used conventionally, in different areas of engineering. The best part is
thwat the theory of fuzzy control provides non-linear controllers, having the
capability to perform various non-linear complex control action, also for nonlinear
systems that are uncertain. FLC design demands no precise system model
knowledge, like system’s zero and poles of transfer functions, unlike the
conventional control. Fuzzy control system have the two critical inputs, in its
design, that are rate change of the error and tracking error, in the way of human
learning imitation4.
ADVANTAGES
FLC provide the advantages, as the following.
1. Faster system response
2. Simplicity in the design
3. Provides hint of intelligence of human to the controller
4. Needs no mathematical modelling, related to the system
5. Cost effective
6. Increased reliability of the system
7. Increased precision degree
8. Easy handling of the non-linearity of the system
9. Makes use of linguistic variables, in place of numerical ones
Having all the above advantages, fuzzy controllers are allowed in the
systems, where the process parameters identification with parameters and process
4 Thana Pattaradej, Guanrong Chen and PitikhateSooraksa, "Design and
Implementation of Fuzzy PID Control of a bicycle robot", (2002) Integrated
computer-aided engineering, Vol.9, No.4.

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description are highly difficult. Thus, control mechanism can be obtained by the
fuzzy characteristics [13].
CHALLENGES OF FUZZY TECHNIQUES
Fuzzy logic has been experiencing continued success in wider range of
applications and so gaining acceptance, in the community of the control
engineering, but, there are certain inherent difficulties of the fuzzy techniques, in
terms of approaching difficulties, that have been restricting them to grow. The
difficulties of the fuzzy techniques are as the following that face the difficultness in
the development of the applications.
1. Difficulties to select appropriate function shapes of membership
2. Difficulties to fuzzy rules development, by hand, when larger systems are
considered
3. Difficulties, in terms of fuzzy solutions fine tuning, when certain degree and
levels of accuracy are specified, and when the robustness or reliability has to
be guaranteed, of the solutions. The method of trial and error, still stands to
be the basic method, towards expert knowledge improvement, for stable and
tuned fuzzy controller development.
FUZZY LOGIC APPLICATIONS
1. Subway train
Enhances the accuracy of stop and increases the stable drive with evaluation
of conditions of passenger traffic. Gives a better and smoother stop and
smoother start
2. Video camcorder

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Determination of the best lighting and focusing, during the picture
movement
3. Television
Higher precision of adjustment of colour, brightness and contrast, related to
the picture, to the please viewers
4. Motor control
Enhances and improves the motion control range and accuracy, when
unexpected conditions are occurred
5. Washing machine
Making adjustment of the washing cycle, through fabric type, load size and
dirt judgement
OBJECTIVE
The project has the following objectives.
1. To model the existed BLDC DC motor, separately
2. To control of the speed of BLDC DC motor through typical conventional
methods of controlling
3. To control the speed of the BLDC DC motor, with the controller of FUZZY-
PID
4. To analyze the MF’s sensitivity, evaluation and comparison, considering
difference kinds of them, in the speed control of the fuzzy PID BLDC DC
motor
5. To compare the various techniques of the speed controlling

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PROBLEM STATEMENT
When the typical control techniques are considered, the usage of the PID
controller was to use as a structure of standard control. Because of this external
disturbances and variation of parameters, in this process, the industrial machinery
performance would be distorted greatly and it results in the reduction of the
efficiency. When the new control technique is considered, with the fuzzy and PID
controllers, using as a conventional technique extension, as it preserves the PID
controller linear structure. The fuzzy and PID controllers have been designed, with
the fuzzy logic control basic principle, towards gaining a new controller, similar to
the controllers of the digital PID, possessing analytical formulas. The fuzzy PID
controllers possess control gains varied, in linear structure of them. Varied error
signal rages and errors nonlinear functions are some of the variable gains and
eventually helps in overall performance improvement, because of characteristics
features of them, such as, mechanism of self-tuning that can be adaptable towards
rapid error changes and error rate of change, by effects of time delays,
uncertainties and nonlinearities, related to the process5.
A remarkable disadvantage possessed by these fuzzy logic based methods is
the appropriate tools lacking, for the analysis of performance of the controllers,
like robustness, optimality, stability, etc. One of the significant things is to have the
5 Rahul Malhotra,Tejbeer Kaur, “Dc Motor Control Using Fuzzy Logic
Controller”, (Ijaest) International Journal Of Advanced Engineering Sciences
And Technologies Vol No. 8, Issue No. 2, 291 – 296.

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right choice of the rule based and membership functions parameters, due to the fact
that the control of the fuzzy logic, stands similar to the control algorithm, stands on
the strategy of linguistic control, deriving from the knowledge of expert, into the
strategy of automatic control. The FLC operation is according to the qualitative
knowledge, regarding the controlling system. It needs the application of the
adequate experience and knowledge, to ensure good response, obtained by the
system.
The application of PID controller cannot be with the system, having faster
parameters change, as it demands PID contrast change, in terms of time. Hence,
further study of the Fuzzy controllers and PID controller possible combinations. It
indicates that the system has to be controlled well, by PID that is in turn supervised
by the system of fuzzy6.
There are various kinds of MF (Membership Functions) proposed for the
system of fuzzy control. The provision of MFs custom design is possible, in certain
software of the fuzzy control. The fuzzy control literature gives indication of
various kinds of MFs applications7.
6 Varuneet Varun, G. Bhargavi, Suneet Nayak, “Speed Control Of Induction
Motor UsingFuzzy Logic Approach”.
7 Tushir Meena, Srivastava Smriti, “Design And Simulation Of A Novel
Clustering Based Fuzzy Controller For DC Motor Speed Control”, (2011)
Innovative Systems Design And Engineering, ISSN 2222-1727 (Paper) ISSN
2222-2871 (Online) Vol 2, No 7.

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REFERENCES
1. Zhoa Jin and K. Bose, Bimal Evaluation Of Membership Function For Fuzzy Logic
Controlled Induction Motor.
2. Varun Varuneet, Bhargavi G. and Suneet Nayak, “Speed Control Of Induction Motor
UsingFuzzy Logic Approach.
3. Malhotra Rahul and Kaur Tejbeer, ‘Dc Motor Control Using Fuzzy Logic Controller,
(Ijaest)’ International Journal Of Advanced Engineering Sciences And Technologies Vol
No. 8, Issue No. 2, 291 – 296.
4. Alloua Bomediene and Abderrahamani ABdessalam, ‘Neuro-Fuzzy DC Motor speed
Control Using Particle Swarm Optimization, Leonaro Electronic Journal of Practices
and Technologies ISSN,1583-1078.
5. Pattaradej Thana, Chen Guanrong and Sooraksa Pitikhate, Design and Implementation of
Fuzzy PID Control of a bicycle robot, (Integrated computer-aided engineering, (2002)
Vol.9, No.4,
6. J. Zhang, N. Wang and S. Wang, “A developed method of tuning PID controllers with
fuzzy rules for integrating process, ” ( 2004) Proceedings of the American Control
Conference, Boston, pp. 1109-1114.
7. B.J. Chalmers, “Influence of saturation in brushless permanent magnet drives”, (IEE
proc. B, 1992), Electr.Power Appl, vol.139, no.1.
8. K.H. Ang, G. Chong and Y. Li, “PID control system analysis, design and technology,”
(IEEE transaction on Control System Technology, 2005), Vol.13, No.4, pp. 559-576.
9. Tushir Meena, Srivastava Smriti, “Design And Simulation Of A Novel Clustering Based
Fuzzy Controller For DC Motor Speed Control”,( Innovative Systems Design And
Engineering, 2011) ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online) Vol 2, No 7,
10. M. Chow and A. Menozzi, “On The Comparison Of Emerging And Conventional
Techniques For DC Motor Control” (proc.IECON, 1992) PP.1008- 1013,.
11. Essam Natsheh, Khalid A. Buragga, “Comparison between Conventional and Fuzzy
Logic PID Controllers for Controlling DC Motors”, (2010) IJCSI International Journal
of Computer Science Issues, Vol. 7, Issue 5.
12. Haytham M. Fayek, I. Elamvazuthi, “Type-2 Fuzzy Logic PI (T2FLPI) Based DC
Servomotor Control”, (2012) Journal of Applied Sciences Research, 8(5):2564- 2574.
Dr. R. Arulmozhiyal,R. Kandiban, “An Intelligent Speed Controller for Brushless DC
Motor”, 978-1-4577-2119-9/12,2012,IEEE.
13. Malkeet Saini,Neeraj Sharma, “Speed Control of Separately excited D.C Motor Using
Computional Method”, (2012) Vol 1 Issue 7.