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DESIGN OF FUZZY PID CONTROLLER FOR
SPEED CONTROL OF BLDC MOTOR
PHASE I REPORT
Submitted by
ARJUN M
Register No. 710012428003 in partial fulfilment for the award of the degree of MASTER OF ENGINEERING in CONTROL AND INSTRUMENTATION

DEPARTMENT OF ELECTRICAL AND ELECTRONICS
ENGINEERING
ANNA UNIVERSITY
REGIONAL CENTRE, COIMBATORE
COIMBATORE-641 047
DECEMBER 2013

ii

ANNA UNIVERSITY
REGIONAL CENTRE, COIMBATORE
COIMBATORE-641 047
DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING

PROJECT WORK

PHASE I
DECEMBER 2013

This is to certify that the project entitled
DESIGN OF FUZZY PID CONTROLLER FOR SPEED CONTROL OF
BLDC MOTOR is the bonafide record of project work done by
ARJUN M
Register No: - 710012428003
Of M.E. (CONTROL AND INSTRUMENTATION) during the year 2013-2014

Head of the Department
Dr.S.N.DEEPA, M.E., Ph.D.,

Project Guide
Mr.P.HARI KRISHNAN, M.E.,

Submitted for the Project Viva-Voce examination held on

Internal Examiner

External Examiner

iii

DECLARATION

I affirm that the project

titled

DESIGN OF FUZZY PID

CONTROLLER FOR SPEED CONTROL OF BLDC MOTOR being submitted in partial fulfilment for the award of Master of Engineering (M.E.) in Control and Instrumentation is the original work carried out by me. It has not formed the part of any other project work submitted for award of any degree or diploma, either in this or any other University.

Signature of the Candidate
ARJUN M
Register No.710012428003

I certify that the declaration made above by the candidate is true

Signature of the Guide
Mr.P.HARI KRISHNAN, M.E.,

iv

ABSTRACT

This project proposed an improved Fuzzy PID controller to control speed of Brushless DC motor. Brushless DC (BLDC) motors are widely used for many industrial applications because of their high efficiency, high torque and low volume. The proposed controller is called proportional-integral-derivative
(PID) controller and Fuzzy proportional-integral-derivative controller. This paper provides an overview of performance conventional PID controller and
Fuzzy PID controller. It is difficult to tune the parameters and get satisfied control characteristics by using normal conventional PID controller. As the
Fuzzy has the ability to satisfied control characteristics and it is easy for computing, In order to control the BLDC motor, a Fuzzy PID controller is designed as the controller of the BLDC motor. The experimental results verify that a Fuzzy PID controller has better control performance than the conventional PID controller. The modelling, control and simulation of the
BLDC

motor

have

MATLAB/SIMULINK.

been

done

using

the

software

package

v

ACKNOWLEDGEMENT

I wish to express my deep sense of gratitude and hearted thanks to
Dr.M.SARAVANAKUMAR, M.B.A., Ph.D., Regional Director, Anna
University, Regional Centre Coimbatore, for providing necessary facilities in the university to complete my project work successful.
I would like to thank Dr.S.N.DEEPA, M.E., Ph.D., and Head of the
Department, Electrical and Electronics Engineering for her kind support and for providing necessary facilities to carry out the work.
I would like to express my sincere thanks and deep sense of gratitude to my gifted guide Mr.P.HARI KRISHNAN, M.E., Assistant Professor,
Department of Electrical and Electronics Engineering, for his valuable guidance, suggestions and constant encouragement paved way for the successful completion of the project work.
I also extend my sincere thanks to the entire Faculty Members of
Electrical and Electronics Engineering and department friends who have render their valuable help in completion of this project.
I am personally indebted and love to record my deepest gratitude to the
Lord Almighty and My Parents who have given me endless support, and provided me with an opportunity to reach this far with my studies.

vi

TABLE OF CONTENTS

CHAPTER NO.

TITLE

PAGE NO.

ABSTRACT

iv

LIST OF TABLES

viii

LIST OF FIGURES

ix

LIST OF ABBREVATIONS

xi
1

Brief introduction of BLDC motor

1

1.2

Scope of the project

2

1.3
2

INTRODUCTION
1.1

1

Literature survey

2

SPEED CONTROL SYSTEM OF BLDC MOTOR

5

2.1

Block Diagram Description

5

2.1.1 Brushless DC motor

6

2.1.2 Components of PMBLDC motor

8

2.1.3 Types of PMBLDC Motor

8

2.1.4 Operating Principles of PMBLDC Motor

9

2.1.5 Variants of PMBLDC Motors

9

2.1.6 Hall sensors in BLDC motor

10

2.1.7 Introduction about PID controller

11

2.1.8 Tuning of PID controller

12

2.1.9 Design of PID Controller

13

2.1.10 Overview of Fuzzy Logic

16

2.1.11 Fuzzy System

19

vii

2.1.12 Fuzzy PID Controller for Speed Control
2.1.13 Fuzzy Rules and Membership Functions

21

Implementation of Speed Control in MATLAB

23

2.2.1 Simulink model of PID controller

24

2.2.2 Simulink model Implemented in Hall sensor

25

2.2.3 Simulink model for PWM

2.2

20

25

2.2.4 Simulink model of Fuzzy PID controller for speed control 2.2.5 Simulink model of Fuzzy PID controller
3

26
27

RESULTS AND DISCUSSIONS

28

3.1

Performance of PID controller

28

3.2

Performance of Fuzzy PID controller

31

3.3

Conclusion

34

3.4

Future Work

34

REFERENCES

35

viii

LIST OF TABLES

FIGURE NO.

TITLE

PAGE NO

2.1

Clock wise Rotation

6

2.2

Gate Logic

6

2.3

PID values

14

2.4

Table of Fuzzy Rules

23

ix

LIST OF FIGURES
FIGURE

TITLE

NO.

PAGE
NO.

2.1

Block diagram

5

2.2

Components of PMBLDC Motor

8

2.3

Simulation Model of PID controller

13

2.4

Input / Output Mapping Problem

17

2.5

Structure of Fuzzy Logic Control

17

2.6

Types of Membership function

18

2.7

Membership function

19

2.8

Block diagram of Fuzzy PID controller for BLDC motor 21

2.9

Structure of Fuzzy PID controller

22

2.10

Member ship functions of output

22

2.11

Simulink model of speed control of BLDC motor using
PID controller

24

2.12

Simulink Model for Hall sensor

25

2.13

Simulink model of PWM

26

2.14

Simulink model of speed control of BLDC motor using
Fuzzy PID controller

27

2.15

Simulink model of Fuzzy PID controller

27

3.1

Speed characteristics with reference Speed of 3000rpm with no load

3.2

Speed characteristics with Step down Speed of

28

x

3000 – 2500 rpm with no load
3.3

Torque characteristics with reference Speed of 3000rpm with no load

3.4

32

Speed characteristics with reference Speed of 3000rpm with load of Fuzzy PID controller

3.12

32

Torque characteristics with Step down Speed of
3000 – 2500 rpm with no load of Fuzzy PID controller

3.11

32

Torque characteristics with reference Speed of 3000rpm with no load of Fuzzy PID controller

3.10

31

Speed characteristics with Step down Speed of
3000 - 2500rpm with no load of Fuzzy PID controller

3.9

31

Speed characteristics with reference Speed of 3000rpm with no load of Fuzzy PID controller

3.8

30

Torque characteristics with reference Speed of 3000rpm with no load

3.7

30

Speed characteristics with reference Speed of 3000rpm with load

3.6

29

Torque characteristics with Step down Speed of
3000 – 2500 rpm with no load

3.5

29

33

Torque characteristics with reference Speed of 3000rpm with no load of Fuzzy PID controller

33

xi

LIST OF ABBREVATIONS
BLDC

Brush less Direct Current Motor

PM

Permanent Magnet

ECM

Electronically Commutated Motor

PMSM

Permanent Magnet Synchronous Motor

PID

Proportional Integral Derivative

PI

Proportional Integral

MOSFET

Metal Oxide Semiconductor Field Effect Transistor

MATLAB

Matrix Laboratory

EMF

Electro Magnetic Force

FLC

Fuzzy Logic Control

FLSC

Fuzzy Logic Speed Controller

PWM

Pulse Width Modulation

1

CHAPTER 1
INTRODUCTION
1.1 Brief introduction of BLDC motor
Brushless DC motors are applicable in key applications of critical importance, such as aerospace industry, tool drives, actuators and electric vehicle propulsion system since these need to cater to servo applications. Hence, the necessity for precise control with quick response time is evident and obvious.
Further these applications warrant the weight-density to be low and torque speed characteristics to be good. Also the inherent disadvantages of the conventional dc machines which necessitate the use of mechanical brushes and commutator problems has obviated these motors applied to such high performance applications. There are mainly two types of dc motors used in industry. The first one is the conventional dc motor where the flux is produced by the current through the field coil of the stationary pole structure. The second type is the brushless dc motor where the permanent magnet provides the necessary air gap flux instead of the wire-wound field poles. BLDC motor is conventionally defined as a permanent magnet synchronous motor with a trapezoidal Back EMF waveform shape. As the name implies, BLDC motors do not use brushes for commutation; instead, they are electronically commutated. Recently, high performance BLDC motor drives are widely used for variable speed drive systems of the industrial applications and electric vehicles.

2

In practice, the design of the BLDCM drive involves a complex process such as modelling, control scheme selection, simulation and parameters tuning etc. Recently, various modern control solutions are proposed for the speed control design of BLDC motor. However, Conventional PID controller algorithm is simple, stable, easy adjustment and high reliability, Conventional speed control system used in conventional PID control. But, in fact, most industrial processes with different degrees of nonlinear, parameter variability and uncertainty of mathematical model of the system. Tuning PID control parameters is very difficult, poor robustness, therefore, it's difficult to achieve the optimal state under field conditions in the actual production.
1.2 Scope of the project
In this project Fuzzy PID control is introduced in speed regulation system of BLDC motor. Here common set of rule are formed for Kp, Ki and Kd. The aim of this paper is that it shows the dynamics response of speed with design the
Fuzzy PID controller to control a speed of motor for keeping the motor speed to be constant when the load varies. The simulation result show that the performance of the Fuzzy PID controller has been has better control performance than the conventional PID controller.
1.3 Literature Survey
[1]

Kusko. A, Peeran. S.M, (1988) “Definition of the Brushless Dc Motor”, gives the evolution of the definitions of PMBLDC motor over various stages and the fundamentals on the controller aspects which need to be viewed more as a PMBLDC system than as a PMBLDC motor.

[2]

Krause. P.C. And Wasynczuk. O, (1989) “Electromechanical Motion
And Devices”, New York: Mc. Graw Hill, has provided in detail the theory and concepts of the mathematical aspects of the PMBLDC motor with various factors.

[3]

Bose. B.K, (1986) “Power Electronics And Ac Drives”, Englewood
Cliffs, Nj:Prentice-Hall, has provided in details on the types of controller

3

and in specific the hysteresis band control for the implementation of speed control in PMBLDC motor.
[4]

George Stephanopoulous, (1998) “Chemical Process Control”, New
Delhi: Prentice-Hall, has provided details on PID.

[5]

Awadallah. M.A, Morocs. M.M, (December 2002) “Adaptive Fuzzy
Based Stator Winding Fault Diagnosis Of PMBLDC Motor Drive By
Monitoring Supply Current”, IEEE Transactions On Power Engineering
Review, December 2002, provides details on the implementation of speed control of fuzzy logic system.

[6]

Soni Monika Gordhandas, “Speed Control of BLDC Motor using
Fuzzy Logic Controller”, speed is control by fuzzy logic is nearly accurate than conventional control. PI&PID controller (conventional) don’t give accurate answer. Get good dynamic response of speed by fuzzy logic. The speed is detect by hall sensors is very good than complicated encoder system. Fuzzy control gives better output than conventional methods like PI, PID .Here rule table for different value of speed & get accurate answer for the control of speed. She use mamdani methods for fuzzy control & centroid method for defuzzification.

[7]

Pooja Agarwal, Arpita Bose, “Brushless Dc Motor Speed Control
Using Proportional-Integral and Fuzzy Controller”, IOSR Journal of
Electrical and Electronics Engineering, Provides the detailed analysis of
Proportional-Integral (PI) controller and Fuzzy Logic controller for speed control of a Permanent Magnet Brushless DC (PMBLDC) motor for different speed commands and varying load torque conditions.
Implementation of PI controller in closed loop conditions is done. Analysis of classical tuning methods to obtain best PI parameters for speed control is calculated. Fuzzy controller is also implemented for the same and the simulation results obtained for both PI and Fuzzy control in
MATLAB/Simulink are compared. PI controllers have poor response due

4

to overshoot, more drop in speed and oscillations. Intelligent control like
Fuzzy Logic is gaining momentum as it can overcome these disadvantages.
[8]

M. V. Ramesh, J. Amarnath, S. Kamakshaiahand G. S. Rao, “Speed control of Brushless DC motor by using Fuzzy logic pi controller”,
ARPN Journal of Engineering and Applied Sciences, Provides the fuzzy,
PI controller for speed control of BLDC motor. The controller uses three fuzzy logic controllers and three PI controllers. The output of the PI controllers is summed and is given as the input to the current controller.
The current controller uses P controller. The BLDC motor is fed from the inverter where the rotor position and current controller is the input. The fuzzy logic control is learned continuously and gradually becomes the main effective control.

5

CHAPTER-2
SPEED CONTROL SYSTEM OF BLDC MOTOR
2.1 Block Diagram Description
The complete block diagram of speed control of three phase BLDC Motor is below Figure.2.1. Two control loops are used to control BLDC motor. The inner loop synchronizes the inverter gates signals with the electromotive forces.
The outer loop controls the motor's speed by varying the DC bus voltage. ὠr PID /FUZZY PID
CONTROLLER

INVERTER

BLDC MOTOR

ὠm
TRIGGERING
SIGNAL
POSITION
SENSOR

HALL EFFECT
SENSOR
Inner loop
Outer loop

Figure.2.1 Block Diagram
Driving circuitry consists of three phase power convertors, which utilize six power transistors to energize two BLDC motor phases concurrently. The rotor position, which determines the switching sequence of the MOSFET transistors, is detected by means of 3 Hall sensors mounted on the stator. By using Hall sensor information and the sign of reference current (produced by Reference current generator), Decoder block generates signal vector of back EMF. The basic idea of running motor in opposite direction is by giving opposite current. Based on

6

that, we have Table.2.1 for calculating back EMF for Clockwise of motion and the gate logic to transform electromagnetic forces to the 6 signal on the gates is given Table.2.2.
Hall
sensor
A
0
0
0
0
1
1
1
1

Hall
Sensor
B
0
0
1
1
0
0
1
1

Hall
Sensor
C
0
1
0
1
0
1
0
1

EMF
A

EMF
B

EMF
C

0
0
-1
-1
1
1
0
0

0
-1
1
0
0
-1
1
0

0
1
0
1
-1
0
-1
0

Table.2.1 Clockwise Rotation
EMF
A
0
0
-1
-1
1
1
0
0

EMF
B
0
-1
1
0
0
-1
1
0

EMF
C
0
1
0
1
-1
0
-1
0

Q1

Q2

Q3

Q4

Q5

Q6

0
0
0
0
1
1
0
0

0
0
1
1
0
0
0
0

0
0
1
0
0
0
1
0

0
1
0
0
0
1
0
0

0
1
0
1
0
0
0
0

0
0
0
0
1
0
1
0

Table.2.2 Gate Logic
2.1.1 Brushless DC motor
In many adjustable speed drives the demand is for precise and continuous control of speed with long-term stability, good transient performance and high efficiency. The dc motor has satisfied some of these requirements, but due to the

7

presence of commutator and brushes dc motors have a number of disadvantages as compared to ac motors. However permanent magnet motors which have permanent magnet on the rotor have the following advantages over induction motor. The rare earth and neodymium boron permanent magnet has low inertia when compared with an Induction motor because of the absence of rotor cage; this makes faster response for a given torque. In other words, the torque to inertia ratio of these permanent magnet machines is higher.
The permanent magnet machine has a higher efficiency than an induction machine. This is primarily because there are negligible rotor losses in permanent magnet machines.
The induction motor requires a source of magnetizing current for excitation. The permanent magnet machine already has the excitation in the form of the rotor magnet.
The permanent magnet machine is smaller in size than an induction motor of the same capacity. Hence it is advantageous to use permanent magnet machines, especially where space is a serious limitation. In addition, the permanent magnet machine weight is less. In other words, the power density of permanent magnet machine is higher. Because of above mentioned advantages, enhanced the brushless dc motor for adjustable speed applications.
There are two types of permanent magnet motors. They are PMSM
(Permanent Magnet Synchronous Motor) and PMBLDC (Permanent Magnet
Brushless DC Motor).Depending upon the application a choice is made between a PMSM and PMBLDC. An electric drive system is considered “high performance” when rotor speed can be made to follow preselected trajectory. This is essential in applications such as robotics, guided manipulation and dynamic actuation where precise rotor movement must be achieved. Several types of electric motors have been proposed for HPD (High Performance Drive) applications. Apart from above mentioned advantages, advances in high-energy

8

permanent magnet materials and power electronics have widely enhanced the brushless dc motor for these applications.
For this brushless dc motor conventional controllers require accurate mathematical models describing the dynamics of the system under control. Even if a model can be obtained for the system under control one of the main difficulties with conventional tracking controllers for electric drives is their inability to capture unknown load characteristics over a widely ranging operating point. This makes tuning of respective parameters difficult.
2.1.2 Components of PMBLDC Motor
Components of PMBLDC motor are elucidated as shown in figure.2.2 as follows. Figure.2.2 Components of PMBLDC Motor
2.1.3 Types of PMBLDC Motor
Types of PMBLDC motor are classified as shown below.
a. One-phase one-pulse motor
b. One-phase two-pulse motor

9

c. Tow-phase one-pulse motor
d. Three-phase three-pulse motor
e. Four-phase four-pulse motor
f. Three-phase six-pulse motor
2.1.4 Operating Principles of PMBLDC Motor
In self-control of PMBLDC motor as the rotor speed changes, the armature supply frequency is also changed proportionally so the armature field always moves at the same as the rotor. This ensures that the armature and rotor fields move in synchronism for all operating points. The accurate tracking of speed by frequency is realized with the help of a rotor position sensor. The switches of the inverter, feeding the motor are fired based on the rotor position. The frequency of the voltage induced in the armature is proportional to the speed. There are two types of PMBLDC Motor, Exterior rotor brushless dc motor and Interior rotor brushless dc motor.
The exterior rotor type has fixed armature winding on the stator, and rotating magnets on the outside. This type of common hard disk drives in computers. The rotating magnet causing provides a conventional cylindrical form on which to mount the platters, and the large diameter helps to increase the inertia, which in turn helps to maintain constant rotational speed.
The interior rotor type has the magnets on the rotating rotor. Brushes and commutator are not necessary because the windings are in the stator and do not rotate. The small rotor diameter reduces the inertia compared to that of the exterior rotor motor, and this configuration is common in servo-system. The stator is similar to that of an ac induction motor. For low speed operation it is often sufficient to bond the magnets to the surface of the rotor hub.
2.1.5 Variants of PMBLDC Motors
a. Axial-gap disc designs
b. Inside rotor design

10

c. Outside rotor design
d. Slot-less design
Brushless dc motors are similar in construction to permanent magnet synchronous motors, which has a poly-phase winding on the stator and permanent magnets on the rotor. Pulses generated by the rotor position sensor control transistors of the inverter that feeds stator winding. This ensures that rotor revolves at angular speed which is equal to the average speed of the stator field.
Thus, drive operation is free from the problems of pull out and hunting.
The drive functions are similar to a dc motor. Like a dc motor it is fed from a dc source, stator and rotor fields remain stationary with respect to each other at all speeds, and speed torque characteristics are similar and speed can be controlled by the control of dc input voltage. Because of these similarities and owing to the fact that it does not have brushes, the drive is known as brushless dc motor. The permanent magnet synchronous machine block operates in either generating or motoring mode. The sign of the mechanical torque dictates the mode of operation (positive for motoring, negative for generating). The electrical and mechanical parts of the machine are each represented by a second-order statespace model. The model assumes that the flux established by the permanent magnets in the stator is sinusoidal, which implies that the electromotive forces are sinusoidal. The block implements the following expressed in the rotor reference frame.
2.1.6 Hall sensors in BLDC motor
The commutation of a BLDC motor is controlled electronically. To rotate the BLDC motor, the stator windings should be energized in a sequence. It is important to know the rotor position in order to understand which winding will be energized following the energizing sequence. Rotor position is sensed using
Hall Effect sensors embedded into the stator. Most BLDC motors have three Hall sensors embedded into the stator on the non-driving end of the motor as shown in Figure. Whenever the rotor magnetic poles pass near the Hall sensors, they

11

give a high or low signal, indicating the N or S pole is passing near the sensors.
Based on the combination of these three Hall sensor signals, the exact sequence of commutation can be determined.
2.1.7 Introduction about PID controller
The control of electrical motors used in high-performance servo drives and robots, control concepts to achieve high dynamic performance. PID controllers are extensively used in servo control system. The performance of PID controllers is sensitive to system parameter variations. Servomotors are used in many automatic systems, including drives for printers, tape recorders, and robotic manipulators. The development of digital controllers becomes more popular because of their superiority over analog controllers. One obvious reason is that the digital controllers can be used to implement intelligent control algorithm to cope with varying environments as a result of load disturbances, process nonlinearities, and changes in plant parameters.
Servomotors are usually controlled by proportional-integral-derivative algorithms. These algorithms are effective enough if the speed and accuracy requirements of the control system are not critical. The usual way to optimize the control action is to tune the PID coefficients, but this cannot cope with varying control environment or system nonlinearity.
Due to these problems, it is appropriate that incorporating human intelligence into automatic control system would be a more efficient solution, leading to the development of fuzzy control algorithm. The fuzzy algorithm is based on intuition and experience, and can be regarded as set of heuristic decision rules or “rules of thumb”. Such nonmathematical control algorithms can be implemented easily. They are straight forward and generally do not involve any computational problems.

12

2.1.8 Tuning of PID controller
Controller tuning involves the selection of the best values of Kc, Ti and
TD (if a PID algorithm is being used). This is often a subjective procedure and is certainly process dependent.
A possible explanation for this is lack of understanding of process dynamics, lack of understanding of the PID algorithm or lack of knowledge regarding effective turning procedures. This section of the notes concentrates on
PID tuning procedures. The suggestion being that if a PID can be properly tuned there is much scope to improve the operational performance of chemical process plat. When tuning a PID algorithm, generally the aim is to match some preconceived ‘ideal’ response profile for the closed loop system. The following response profiles are typical.
The tuning rules are valid for the ‘ideal’ PID control structure and any predication of control law settings should be adjusted if an alternative PID implementation is used. The tuning rules are only valid for self-regulation processes (i.e. open loop stable process such as those that may be described by
1st order plus dead-time description). Luckily most process system are selfregulation the exception to the rule being level systems. Despite a lot of research and the huge number of different solutions proposed, most industrial control systems are still based on conventional PID regulation. Different sources estimate the share taken by PID controllers at between 90 and 99%. Some of the reasons for this situation may be given as follows.
a. PID controllers are robust and simple design.
b. There exists a clear relationship between PID and system response parameters. As a PID controller has only three parameters, plant operates have a deep knowledge about the influence of these parameters and the specified response characteristics on each other.

13

c. Many PID tuning techniques have been elaborated during recent decades, which facilitates the operator’s task.
Because of its flexibility, PID control could benefit from the advances in technology. Most of the classical industrial controllers have been provided with special procedures to automate the adjustment of their parameters (tuning and self-tuning). 2.1.9 DESIGN OF PID CONTROLLER
Consider the characteristics parameters – proportional (P), integral (I), and derivative (D) controls, as applied to the diagram below in Figure.2.3, the system,

Figure.2.3 Simulation Model of PID controller
A PID controller is simple three-term controller. The letter P, I and D stand for P- Proportional, I- Integral, D- Derivative. The transfer function of the most basic form of PID controller, is

C (S )  K
C (S ) 

K

p

D

S


2

K
S

 K

I

 K
S

P

D

S  K

S

(2.1)
I

(2.2)

Where KP = Proportional gain, KI = Integral gain and KD = Derivative gain.
The control u from the controller to the plant is equal to the Proportional gain

14

(KP) times the magnitude of the error pulse the Integral gain (KI) times the integral of the error plus the Derivative gain (KD) times the derivative of the error.

de p i  d (2.3) dt Due to its simplicity and excellent if not optimal performance in many

u  K

e  K

edt

 K

applications, PID controllers are used in more than 95% of closed-loop industrial processes We are most interested in four major characteristics of the closed-loop step response. They are
Rise Time: The time it takes for the plant output Y to rise beyond 90% of the desired level for the first time.
Overshoot: Shows how much the peak level is higher than the steady state, normalized against the steady state.
Settling Time: The time it takes for the system to converge to its steady state.
Steady-state Error: the difference between the steady-state output and the desired output. The Values of KP, KI and KD values of PID Controller is shown in below table.2.3 are obtained by using the tuning method.

Controller

KP

KI

KD

PID

0.8

48

0.01

Table.2.3 PID Values
a. Model calculations
The transfer function of BLDC motor is given by

G (S ) 

K
S

2

 JR  BL
 
JL


t

/ JL


 BR
S  



 K tK
JL

e




(2.4)

15

The transfer function of BLDC motor for following parameters:
L=8.5e-3 H, R=0.2 Ώ, J=0.089 kgm2, B=0.005 Nms/rad, Ke=0.175 vs,
Kt =1.4 Nm/A.

G

p

(S ) 

S

2

3 . 717
 0 . 537 S  6 . 5

(2.5)

Closed loop transfer function is formed by

G(S ) 
G(S) 

KGp (S )
1  KGp (S )

K (3.717)
S  0.537S  6.5  3.717K
2

(2.6)
(2.7)

Characteristic equation for closed loop system is given by
S2 + 0.537S + 6.5 + 3.717K = 0

(2.8)

By using Routh array criterion,
Value of K is found as
K = 0.94
Substitute K in equation (2.8),
We get,
S2 + 0.537S + 10 = 0

(2.9)

Second order Characteristic equation is given as
S2 + 2£ὠn + ὠn2 = 0
Compare (2.9) and (2.10) equations,
We get, ὠn = 3.16
Using Ziegler Nicholas Method,
For PID controller,
Kp = 0.6 K
Ki = 2 Kp / Pcr = Kp / Ti

(2.10)

16

Kd = Kp Td = Kp /8 Pcr
Ti = Pcr / 2
Td = Pcr / 8
Pcr = 2∏ / ὠn
Substitute K and ὠn values in Kp,Ki,Kd,
We get Kp = 0.8
Ki = 48
Kd = 0.01
General form:

C (S ) 

K

D

S

2

 K
S

P

S  K

I

(2.11)

Substituting above values,

C (S ) 

0 . 01 S

2

 0 . 8 S  48
S

(2.12)

2.1.10 Overview of Fuzzy Logic
Fuzzy logic (FL) is one of the artificial intelligent techniques. Fuzzy logic unlike Boolean logic, deals with problems that have fuzziness or vagueness. The classical set theory is based on Boolean logic, where particular object or variable is either a member of a given set (logic 1), or it is not (logic 0). On the other hand, in fuzzy set theory based on fuzzy logic, a particular object has a degree of membership in a given set that may be anywhere in the range of 0 (completely not in the set) to 1 (completely in the set). For this reason, FL is often defined as multi-valued logic, compared to bi-valued Boolean logic.
A FL problem can be defined as an input/output, static, nonlinear problem through a “black box”. All input information is defined in the input space, it is processed in the black box, and the solution appears in the output space. In general, mapping can be static or dynamic, and the mapping characteristic is determined by the black box’s characteristics. The black box cannot only be a

17

fuzzy system, but also an Expert System (ES), neural network, general mathematical system, such as differential equations, algebraic equations, etc., or anything else. FL processing is shown in Figure.2.4

Fig.2.4 Input / Output Mapping Problem
Fuzzy Logic Control gives superior results with respect to conventional control algorithms thus, in industrial electronics the FLC control has become an attractive solution in controlling the electrical motor drives. With by this logic we can get accurate answer than other methods. Fuzzy logic techniques have gained much interest in the application of control system. They have a real time basis as a human type operator, which makes decision on its own basis. The structure of this system is shown below,

Figure.2.5 Structure of Fuzzy Logic Control

18

It has three main component,
a. Fuzzifier
b. Inference engine.
c. Defuzzifier.
By this structure, first we convert crisp value into fuzzy value .it is called fuzzification, & last we convert fuzzy value into crisp value, called defuzzification. Between this two block we do decision making process, in this process we make rule base & get the accurate result.
Fuzzy logic terms are expressed in the form of logical implication. Such as if- then rules. It is called member ship function. Which are shown below,
a.Triangular function b.trapazoidal function c.Bellshape function

Figure.2.6 Types of Membership function
a. Defuzzification
It converts fuzzy value into crisp value there are three methods for defuzzification, I.

The max criterion method
It produce a point at which membership function reaches maximum value. 19

II.

The height method
The centroid of each membership function for each rule is first evaluated. The final output is then calculated as the average of the individual centroids.

III.

Centroid method
It generate the centre of gravity of the area by membership function.
There are seven clusters in the membership functions, with seven linguistic variables defined as: Negative Big (NB), Negative (N),
Negative Small (NS), Zero (Z), Positive Small (PS), Positive (P), and
Positive Big (PB). Which are shown in below figure,

Figure.2.7 Membership function
2.1.11 Fuzzy System
A fuzzy inference system (or fuzzy system) basically consists of a formulation of the mapping from a given input set to an output set using FL. This mapping process provides the basis from which the inference or conclusion can be made.
A fuzzy inference process consists of the following five steps.
a. Fuzzification of input variables.
b. Application of fuzzy operator (AND, OR, NOT) in the IF (antecedent part of the rule.
c. Implication from the antecedent to the consequent (THEN part of the rule).
d. Aggregation of the consequents across the rules.

20

e. Defuzzification.
There are number of implication methods and defuzzification methods.
Depending on the requirement choice is made on the implication and defuzzification methods.
The control algorithm of process that is based on FL or fuzzy inference system is defined as a fuzzy control. In general, a control system based on
Artificial Intelligent (AI) is defined as intelligent control. A fuzzy control essentially embeds the experience and intuition of a human plant operator, and sometimes those of a designer and/or researcher of a plant. The design of a conventional control system is normally based on the mathematical model of a plant. Power electronics system models are ill defined. Even if a plant model is well known there may be parameter variation problems. Sometimes, the model is multivariable, complex, and non-linear, such as dynamic d-q model of an ac machine. Vector or field oriented control of a drive can overcome this problem, but accurate vector control is nearly impossible, and there may be wide parameter variation problem in the system. To combat such problems, various adaptive control techniques were used. On the other hand, fuzzy control is basically an adaptive and nonlinear control, which gives robust performance for a linear or nonlinear plant with parameter variation. FL applications in power electronics and motor drives are somewhat recent. Fuzzy adaptive, hybrid fuzzy controller gathered momentum to these power electronics and drives areas.
From the above it is clear that, the advent of AI technology has brought new challenge to power electronics engineers who are struggling with complex, fast advancing technology. Apart from the above mentioned techniques there are many other techniques to solve the complex problems.
2.1.12 Fuzzy PID Controller for Speed Control
In industrial electronics the FL control has become an attractive solution in controlling the electrical motor drives. However, the FL controllers design and

21

tuning process is often complex because several quantities such as membership functions, control rules and input and output gains must be adjusted. But it gives good response compared to that of conventional controller both in simulations and experimentally. So, it is preferred. The block diagram of Fuzzy Logic Speed
Controller (FLSC) for brushless DC motor is shown in figure. 2.8 ὠr FUZZY PID
CONTROLLER

INVERTER

BLDC MOTOR

ὠm
TRIGGERING
SIGNAL
POSITION
SENSOR

HALL EFFECT
SENSOR
Inner loop
Outer loop

Figure.2.8 Block diagram of Fuzzy PID controller for BLDC motor
The winding of brushless DC motor is supplied from three-phase voltage source inverter. The phase currents are controlled by a constant band hysteresis regulator. The current commands are imposed by outer speed control loop. The
FLSC gives an incremental control action by processing the speed error and the change in speed error. The output of FLSC gives reference current magnitude for q-axis current. In the air gab flux mode d-axis current is zero. Like previous controller transformation and control of current controller takes place.
2.1.13 Fuzzy Rules and Choice of Membership Functions
Fuzzy PID controller used in this project is based on two inputs and one output. The overall structure of used controller is shown in Fig. 2.10. In Fuzzy
PID controller only one output which are connected to Kp, Ki and Kd. Real interval of variables is obtained by using scaling factors which are Se, Sde and Su. The fuzzy control rule is in the form of: IF e=Ei and ce=dEj THAN UPD=UPD (i, j).

22

These rules are written in a rule base look-up table which is shown in Fig. 2.9.
The rule base structure is Mamdani type.

Figure.2.9 Structure of Fuzzy PID controller
FLC has two inputs and one output. These are error (e), error change (de) and control signal, respectively. A linguistic variable which implies inputs and output have been classified as: NB, NM, NS, Z, PS, PM, PB. Inputs and output are all normalized in the interval of [-3, 3] as shown in Figure. 2.10.

Figure.2.10 Member ship functions of output

23

The linguistic labels used to describe the Fuzzy sets were „Negative Big‟
(NB), „Negative Medium‟ (NM), „Negative Small‟ (NS), „Zero‟ (Z), „Positive
Small‟ (PS), „Positive Medium‟ (PM), „Positive Big‟ (PB). It is possible to assign the set of decision rules as shown in Table IV. The fuzzy rules are extracted from fundamental knowledge and human experience about the process. These rules contain the input/the output relationships that define the control strategy.
Each control input has seven fuzzy sets so that there are at most 49 fuzzy rules.
E/CE

NB

NM

NS

Z

PS

PM

PB

NB

NB

NB

NB

NB

NM

NS

Z

NM

NB

NB

NB

NM

NS

Z

PS

NS

NB

NB

NM

NS

Z

PS

PM

Z

NB

NM

NS

Z

PS

PM

PB

PS

NM

NS

Z

PS

PM

PB

PB

PM

NS

Z

PS

PM

PB

PB

PB

PB

Z

PS

PM

PB

PB

PB

PB

Table.2.4 Table of Fuzzy Rule
2.2 Implementation of Speed control in MATLAB
MATLAB/Simulink is an environment for multi domain simulation and model based design for dynamic and embedded systems. It provides an interactive graphical environment and a customizable set of block libraries that let you design, stimulate, implement and test a variety of time varying systems, including communications, controls, signal processing, video processing and image processing. So we implement Speed control of BLDC motor using
MATLAB.

24

2.2.1 Simulink model of PID controller

Figure.2.11 Simulink model of speed control of BLDC motor using PID controller MATLAB/ Simulink model of speed control of BLDC motor using PID controller shown in Figure.2.11 is consist of BLDC motor, PID controller model,
Hall sensor model, MOSFET ,PWM controller and scopes. These models are simulated by using MATLAB and the speed and torque wave forms are noted by using scope. The Simulink models are taken from the Simulink library. The

25

reference speed given to Simulink model of speed control of BLDC motor using
PID controller is 3000rpm.
2.2.2 Simulink model Implemented in Hall sensor
Below figure shows how the Simulink model is implemented in Hall sensor for speed control of BLDC motor.

Figure.2.12 Simulink Model for Hall sensor
2.2.3 Simulink model for PWM
Below figure shows how Simulink model is implemented in PWM block. 26

Figure.2.13 Simulink model of PWM
2.2.4 Simulink model of Fuzzy PID controller for speed control
MATLAB/ Simulink model of speed control of BLDC motor using Fuzzy
PID controller is shown in Figure.3.10 is consist of BLDC motor, Fuzzy PID controller model, Hall sensor model, MOSFET ,PWM controller and scopes.
These models are simulated by using MATLAB and the speed and torque wave forms are noted by using scope. The Simulink models are taken from the
Simulink library. The reference speed given to Simulink model of speed control of BLDC motor using Fuzzy PID controller is 3000rpm.

27

Figure.2.14 Simulink model of speed control of BLDC motor using Fuzzy PID controller 2.2.5 Simulink model of Fuzzy PID controller

Figure.2.15 Simulink model of Fuzzy PID controller
Above figure shows how Simulink model is implemented in Fuzzy PID block. 28

CHAPTER-3
RESULTS AND DISCUSSION
3.1 Performance of PID controller
At no load condition
Performance of the PID Controller of BLDC Motor on speed of 3000rpm and step down speed of 2500 with no load condition of speed and Torque is shown in figures 3.1, 3.2, 3.3 and 3.4. The results shows that conventional PID controller, reach settling time is 0.40 sec.

s p e e d
(rpm)

Time (sec)

Figure.3.1 Speed characteristics with reference Speed of 3000rpm with no load

29

s p e e d
(rpm)

Time (sec)

Figure.3.2 Speed characteristics with Step down Speed of 3000-2500 rpm with no load

T o r q u e (Nm)

Time (sec)

Figure.3.3 Torque characteristics with reference Speed of 3000rpm with no load

30

T o r q u e (Nm)

Time (sec)

Figure.3.4 Torque characteristics with Step down Speed of 3000 - 2500rpm with no load
At load condition
Performance of the Conventional PID Controller of BLDC Motor on speed of 3000rpm with load impact condition of speed and Torque is shown in figures
3.5 and 3.6. During running conduction of BLDC motor, suddenly the load of 5
Nm is applied at time of 0.5 sec and released at 0.7 sec. The results show that conventional PID controller reach settling time is 0.40 sec.

s p e e d
(rpm)

Time (sec)

Figure.3.5 Speed characteristics with reference Speed of 3000rpm with load

31

T o r q u e (Nm)

Time (sec)

Figure.3.6 Torque characteristics with reference Speed of 3000rpm with load
3.2 Performance of Fuzzy PID controller
At no load condition
Performance of the Fuzzy PID Controller of BLDC Motor on speed of
3000rpm and step down speed of 2500 with no load condition of speed and
Torque is shown in figures 3.7, 3.8, 3.9 and 3.10. The results show that Fuzzy
PID controller, reach settling time is 0.15 sec which less than the Conventional
PID controller.

s p e e d
(rpm)

Time (sec)

Figure.3.7 Speed characteristics with reference Speed of 3000rpm with no load of Fuzzy PID controller

32

s p e e d
(rpm)

Time (sec)

Figure.3.8 Speed characteristics with step down Speed of 3000 -2500rpm with no load for Fuzzy PID controller

T o r q u e (Nm)

Time (sec)

Figure.3.9 Torque characteristics with reference Speed of 3000rpm with no load of Fuzzy PID controller

T o r q u e (Nm)

Time (sec)

Figure.3.10 Torque characteristics with Step down Speed of 3000-2500rpm with no load of Fuzzy PID controller

33

At load condition
Performance of the Fuzzy PID Controller of BLDC Motor on speed of
3000rpm with load impact condition of speed and Torque is shown in figures 3.11 and 3.12. During running conduction of BLDC motor, suddenly the load of 5 Nm is applied at time of 0.5 sec and released at 0.7 sec. The results show that Fuzzy
PID controller reach settling time is 0.25 sec which is less than Conventional PID controller. s p e e d
(rpm)

Time (sec)

Figure.3.11 Speed characteristics with reference Speed of 3000rpm with load of
Fuzzy PID controller

T o r q u e (Nm)

Time (sec)

Figure.3.12 Torque characteristics with reference Speed of 3000rpm with load of Fuzzy PID controller

34

3.3 CONCLUSION
This project has presented the modelling, simulation of conventional PID controller and Fuzzy PID controller of three phase BLDC Motor. In conventional
PID control it is not necessary to change the control parameters as the reference speed changes. With results obtained from simulation, it is clear that for the same operation condition the BLDC speed control using Fuzzy PID controller technique had better performance than the conventional PID controller mainly when the motor was working at lower and higher speeds. In addition, the motor speed to be constant when the load varies.
3.4 FUTURE WORK
This project is reviewed for better performance of speed control of BLDC motor with the help of Adaptive technique with Fuzzy PID controller which will be designed in the project phase 2.

35

REFERENCES
[1] Ang.K, Chong.G, and Li.Y, “PID control system analysis, design and technology,” IEEE Trans. Control System Technology, vol. 13, pp. 559-576,
July 2005.
[2] Arulmozhiyal.R and Baskaran.K, “Implementation of Fuzzy PI Controller for
Speed Control of Induction Motor Using FPGA”, Journal of Power
Electronics, Vol.10, No.1, Jan 2010, pp.65-71
[3] Atef Saleh Othman Al-Mashakbeh,“ Proportional Integral and Derivative
Control of Brushless DC Motor”, European Journal of Scientific Research 2628 July 2009, vol. 35, pg 198-203.
[4] ChuenChien Lee, “Fuzzy Logic in Control Systems: Fuzzy Logic controller–
Part 1” 1990 IEEE.
[5] ChuenChien Lee, “Fuzzy Logic in Control Systems: Fuzzy Logic controller
Part 2” 1990 IEEE.
[6] Guo.Q.D, Zhao.X.M. BLDC motor principle and technology application [M].
Beijing: China electricity press, 2008.
[7] Miller J.E, "Brushless permanent-magnet motor drives," Power Engineering
Joumal,voI.2, no. 1 , Jan. 1988.
[8] Uzair Ansari, SaqibAlam, Syed Minhaj un Nabi Jafri, ”Modelling and Control of Three Phase BLDC Motor using PID with Genetic Algorithm”, 2011 UK
Sim 13th International Conference on Modelling and Simulation,pp.189-194.
[9] ZdenkoKovaccic and StjepanBogdan, “Fuzzy Controller design Theory and
Applications”, © 2006 by Taylor & Francis Group. International, 2002.

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