A DESIGN PROJECT ON

DC Drive Using Fuzzy Logic Control

NATIONAL INSTITUTE OF TECHNOLOGY KURUKSHETRA-136119

Submitted to: By:

Mr. Rahul Sharma VIPUL NARULA (Roll No.110657)

SAHIL BHATI (Roll No. 110690)

SHREYAS RAJ (Roll No. 110757)

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National Institute Of Technology

Kurukshetra

CERTIFICATE

This is to certify that the report entitled, “DC Drive Using Fuzzy Logic Control”

submitted by Mr. Vipul Narula, Sahil bhati and Shreyas raj in partial fulfilment

of the requirements for the award of Bachelor of Technology Degree in Electrical

Engineering at the National Institute of Technology, Kurukshetra is an

authentic work carried out by them under my supervision and guidance.

To the best of my knowledge, the matter embodied in the report has not been

submitted to any other University / Institute for the award of any Degree or

Diploma.

Date: Mr.

Assistant Professor

Department of Electrical Engineering

National Institute of Technology

Kurukshetra

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ACKNOWLEDGEMENT

WE would like to extend our gratitude and our sincere thanks to our honourable

supervisor Asst. Prof. , Department of Electrical

Engineering. We sincerely thank for his exemplary guidance and encouragement

in every aspects for my career growth. His trust and support inspired us in the

most important moments of making right decisions and we are glad to work

under his supervision.

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CONTENTS

Page No.

List of Figures

List of Tables

Chapter 1. Project Overview 07

1. Introduction

2. Motivation

3. Organisation

Chapter 2. System Description 09

1. Motor Detailed Description

2. Motor Model

3. Problem Formulation

Chapter 3. Fuzzy Controller 14

1. Introduction

2. Membership function and rules

Chapter 4. Model Components 17

1. Scope

2. Multiplexer

3. Derivative Control

4. Fuzzy Logic Toolbox

5. Bus Creator

Chapter 5. Simulation 23

1. Simulink Model

2. Results

Chapter 6. Conclusion 28

Chapter 7. Bibliography 29

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LIST OF FIGURES

Page No.

Figure 1. Separately Excited DC Motor 09

Figure 2. Plot of speed and torque vs. armature current

of a separately excited DC motor

11

Figure 3. Separately Excited DC Motor Model

12

Figure 4. Model of Separately Excited DC Motor

13

Figure 5. Structure of fuzzy logic controller

15

Figure 6. Scope Block on Workspace

17

Figure 7. Function Block Parameters Editor of a Mux

18

Figure 8. Saturation Block on Workspace

19

Figure 9. The Membership Function Editor (top left),

FIS Editor (centre), Rule Editor (top right),

Rule Viewer (bottom left), and Surface Viewer

(bottom right)

20

Figure 10. Block Parameters Editor of a Bus Creator 21

Figure 11. SIMULINK model of fuzzy control D.C.

machine

23

Figure 12. FIS Editor

23

Figure 13. Membership function for input variable “e”

24

Figure 14. Membership function for output variable

“Controls”

24

Figure 15. Membership function for input variable “de”

25

Figure 16. Rule Editor

25

Figure 17. Rule Viewer

26

Figure 18. Surface View for Fuzzy Controller

26

Figure 19. Output of the system

27

Figure 20. Output of Fuzzy Controller

27

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LIST OF TABLES

Page No.

Table 1. Speed Error Variable

15

Table 2. Change in Speed Error Variable

16

Table 3. Output Variables

16

Table 4. Rules

16

Table 5. Output of Saturation Block

19

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CHAPTER 1

PROJECT OVERVIEW

1.1 Introduction This project aims at speed control of a separately excited DC motor using

fuzzy logic control (FLC) based on MATLAB Simulation program. This

method of speed control of a dc motor represents an ideal application for

introducing the concepts of fuzzy logic. The report shows how a commercially

available fuzzy logic development kit can be applied to the theoretical

development of a fuzzy controller for motor speed, which represents a very

practical class of engineering problems. From this it is seen that the

simulation results are similar to the theoretical results which achieve the

optimum control.

1.2 Motivation For The Project Classic Control has proven for a long time to be good enough to handle control

tasks on system control; however his implementation relies on an exact

mathematical model of the plan to be controller and not simple mathematical

operations. The fuzzy logic, unlike conventional logic system, is able to model

inaccurate or imprecise models. The fuzzy logic approach offers a simpler,

quicker and more reliable solution that is clear advantages over conventional

techniques. Fuzzy logic may be viewed as form of set theory.

At the present time, MATLAB Simulation simplifies the scientific

computation, process control, research, industrial application and

measurement applications. Because MATLAB has the flexibility of a

programming language combined with built-in tools designed specifically for

test, measurement and control. By using the integrated MATLAB

environment to interface with real-world signals, analyze data for meaningful

information and share results. Therefore take MATLAB for develop of the

control system that append with fuzzy logic is incoming for modem control

and the advantages in fuzzy control are more robust control method than

usual conventional control to variation of system parameter. This report

presents the experimental results of the fuzzy logic controller using MATLAB

for speed control of Separately Excited DC Motor through fuzzy logic

controller for speed control is used to facilitate and efficiency the

implementation of controllers.

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1.3 Organization of the Report

Chapter 1: This chapter deals with the basic introduction of the work

and need for study on DC Drive using Fuzzy Logic Control. It

also includes the organization of the project work.

Chapter 2: This chapter deals with the system description and problem

formulation for DC Drive using Fuzzy Logic Control. It also

explains the various inputs needed for Fuzzy controller.

Chapter 3: It provides a brief insight into the Fuzzy controller and

defines the rule set.

Chapter 4: This chapter deals with the blocks used in Simulink

workspace for this model.

Chapter 5: This chapter shows the simulation model and also explains

the results obtained.

Chapter 6: This chapter includes the conclusion of the project work.

Chapter 7: Bibliography.

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CHAPTER 2

SYSTEM DESCRIPTION

2.1 Motor Detailed Description Direct Current motors convert electrical energy into mechanical energy and

they works on dc supply. These type of motors are classified as -

Permanent magnet type dc motor

Separately excited dc motor

DC series wound motor

Shunt wound DC motor

Compound DC motor

Like other DC motors, DC separately excited motors also have both stator and

rotor. Stator refers to the static part of motor, which consists of the field

windings and the rotor is the moving armature which contains armature

windings or coils. Separately excited dc motors have field coils similar to that

of shunt wound dc motor. The name suggests the construction of this type of

motor. Usually, in other DC motors, the field coil and the armature coil both

are energised from a single source. The field of these do not need any separate

excitation. But, in separately excited DC motor, separate supply sources are

provided for excitation of both field coil and armature coil. Look carefully at

the diagram. Here, the field coil is energised from a separate DC voltage

source and the armature coil is also energised from another source. Armature

voltage source may be variable but, independent constant DC voltage is used

for energising the field coil. So, these coils are electrically isolated from each

other and this connection is the speciality of this type of DC motor.

Figure 1: Separately Excited DC Motor

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Working principle of Separately excited DC Motor

It is similar to other types of DC motors. That is, when a current carrying

conductor is placed, it experiences a mechanical force. By Fleming’s left hand

rule, the direction of the rotation is occurred. Here the armature winding is the

current carrying coil and the field winding develops the necessary magnetic

field.

Voltage, current and power Equations

In a separately excited motor, armature and field windings are excited form

two different dc supply voltages. In this motor ,

Armature current Ia = Line current = IL = I

Back emf developed , Eb = V – I Ra where V is the supply voltage and Ra is

the armature resistance.

Power drawn from main supply , P = VI

Mechanical power developed , Pm = Power input to armature – power loss

in armature = VI – I2 Ra = I ( V – IRa) = Eb I

Operating characteristics of separately excited dc motor

Both in shunt wound dc motor and separately excited dc motor field is

supplied from constant voltage so that the field current is constant. Therefore

these two motors have similar speed armature current and torque – armature

current characteristics. In this type of motor flux is assumed to be constant.

Speed – armature current (N – Ia) characteristics:

We know that speed of dc motor is proportional to back emf / flux i.e Eb / φ .

When load is increased back emf Eb and φ flux decrease due to armature

resistance drop and armature reaction respectively .However back emf

decreases more than φ so that the speed of the motor slightly decreases with

load.

Torque – armature current ( τ – Ia) characteristics

Here torque is proportional to the flux and armature current. Neglecting

armature reaction, flux φ is constant and torque is proportional to the

armature current Ia . τ – Ia characteristics is a straight line passing through

the origin. From the curve we can see that huge current is needed to start

heavy loads. So this type of motor do not starts on heavy loads.

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Figure 2: Plot of speed and torque vs. armature current of a

separately excited DC motor

Speed control of separately excited dc motor

Speed of this type of dc motor is controlled by the following methods :

Field control methods

Weakening of field causes increase in speed of the motor while

strengthening the field causes decreases the speed. Speed adjustment of this

type of motor is achieved from the following methods:

i. Field rheostat control :- Here a variable resistance is connected in series

with the field coil. Thus the speed is controlled by means of flux variation.

ii. Reluctance control involving variation of reluctance of magnetic circuit of

motor.

iii. Field voltage control by varying the voltage at field circuit while keeping

armature terminal voltage constant.

Armature control methods

Speed adjustment of separately excited DC motor by armature control may

be obtained by any one of the following methods :

i. Armature resistance control:- Here, the speed is controlled by varying the

source voltage to armature. Generally, a variable resistance is provided with

the armature to vary the armature resistance.

ii. Armature terminal voltage control involving variation of variation of

voltage in armature circuit.

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2.2 Motor Model The resistance of the field winding and its inductance of the motor used in

this study are represented by Rf and La respectively in dynamic model.

Armature reactions effects are ignored in the description of the motor. This

negligence is justifiable to minimize the effects of armature reaction since

the motor used has either interpoles or compensating winding. The fixed

voltage Vf is applied to the field and the field current settles down to a

constant value.

A linear model of a simple DC motor consists of a mechanical equation and

electrical equation as follows:

m m m L

dT J B T

dt

aa b a a a

dIV E I R L

dt

Where

Va is the armature voltage (In volt)

Eb is back emf the motor (In volt)

Ia is the armature current (In ampere)

Ra is the armature resistance (In ohm)

La is the armature inductance (In henry)

Tm is the mechanical torque developed (In Nm)

Jm is moment of inertia (In kg/m²)

Bm is friction coefficient of the motor (In Nm/ (rad/sec))

ω is angular velocity (In rad/sec) The dynamic model of the system is

formed using these differential equations.

Figure 3: Separately Excited DC Motor Model

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Figure 4: Block Model of Separately Excited DC Motor

2.3 Problem Formulation A Separately Excited DC motor is taken as a case study and the control is

achieved using intelligent fuzzy logic based controller. The efficiency is

improved by controlling the speed with fuzzy logic controller and results

are shown graphically. The inputs to the Self-tuning Fuzzy Controller are

speed error "e (t)" and Change-in-speed error "de (t)". The input to the

controller is described by:

( ) ( ) ( )r ae t t t

( ) ( ) ( 1)de t e t e t

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CHAPTER 3

FUZZY CONTROLLER

3.1 Introduction Fuzzy logic is a method of rule-based decision making used for expert

systems and process control that emulates the rule-of-thumb thought

process used by human beings. The basis of fuzzy logic is fuzzy set theory

which was developed by Lotfi Zadeh in the 1960s. Fuzzy set theory differs

from traditional Boolean (or two-valued) set theory in that partial

membership in a set is allowed. Traditional Boolean set theory is two

valued in the sense that a member belongs to a set or does not and is

represented by 1 or 0, respectively.

Fuzzy set theory allows for partial membership or a degree of

membership, which might be any value along the continuum of 0 to 1. A

linguistic term can be defined quantitatively by a type of fuzzy set known

as a membership function. The membership function specifically defines

degrees of membership based on a property such as temperature or

pressure. With membership functions defined for controller or expert

system inputs and outputs, the formulation of a rule base of IF-THEN

type conditional rules is done. Such a rule base and the corresponding

membership functions are employed to analyze controller inputs and

determine controller outputs by the process of fuzzy logic inference.

By defining such a fuzzy controller, process control can be implemented

quickly and easily. Many such systems are difficult or impossible to model

mathematically, which is required for the design of most traditional

control algorithms. In addition, many processes that might or might not be

modelled mathematically are too complex or nonlinear to be controlled

with traditional strategies. However, if a control strategy can be described

qualitatively by an expert, fuzzy logic can be used to define a controller

that emulates the heuristic rule-of-thumb strategies of the expert.

Therefore, fuzzy logic can be used to control a process that a human can

control manually with expertise gained from experience. The linguistic

control rules that a human expert can describe in an intuitive and general

manner can be directly translated to a rule base for a fuzzy logic

controller.

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Figure 5: Structure of fuzzy logic controller

3.2 Membership Functions and Rules In order to improve the performance of FLC, the rules and membership

functions are adjusted. The membership functions are adjusted by making

the area of membership functions near ZE region narrower to produce

finer control resolution. On the other hand, making the area far from ZE

region wider gives faster control response. Also the performance can be

improved by changing the severity of rules.

i) Input Variables

a) Speed Error (E) Variable

Table 1:

Fuzzy Set Error Numerical Range Shape of membership

function

Very Low 0.2 to 0.5

1.0 to 1.0

Trapezoidal

Instant -0.01 to 0.00

0.00 to 0.01

Triangular

Very High -1 to -1

-0.5 to -0.2

Trapezoidal

Very Medium Low 0 to 0.2

0.2 to 0.4

Triangular

Very Medium

High

-0.4 to -0.2

-0.2 to 0

Triangular

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b) Change in Speed Error (De) Variable

Table 2:

Fuzzy Set derivative

of Error

Numerical Range Shape of membership

function

High Negative -1 to -1

-1 to 0

Triangular

Error High Positive 0 to 1

1 to 1

Triangular

ii) Output Variables

Table 3:

Output Numerical Range Shape of membership

function

Decrease a Lot -30 to -25

-25 to -20

Triangular

Increase a Lot 20 to 25

25 to 30

Triangular

Decrease Few -15 to -10

-10 to -5

Triangular

Hold -0.1 to 0

0.0 to 0.1

Triangular

Increase Few 5 to 10

10 to 15

Triangular

iii) Rules

Table 4:

e/de Very High Medium

High

Instant Medium

Low

Very Low

High

Negative

Decrease a

Lot

Decrease

Few

Decrease

Few

Increase

Few

Increase a

Lot

High

Positive

Decrease a

Lot

Decrease

Few

Increase

Few

Increase

Few

Increase a

Lot

Hold

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CHAPTER 4

MODEL COMPONENTS

4.1 Scope

The Scope block displays inputs signals with respect to simulation time.

Figure 6: Scope Block on Workspace

The main features of the block are:

Scope window

If a Scope window is closed at the start of a simulation, scope data is still

written to the connected Scope. As a result, if Scope window is opened

after a simulation, the Scope window displays the input signal or signals.

Plotting signals

If the input signal is continuous, the Scope draws a point-to-point plot. If

the signal is discrete, the Scope draws a stair-step plot.

Time step values

The Scope block only displays major time step values. The scope displays

additional interpolated points between major time steps if specified by the

refine parameter.

Multiple y-axes (graphs)

A Scope window can display multiple y-axes (graphs) with one graph per

input port. All of the y-axes have a common time range on the x-axis. The

Scope block allows to adjust the amount of time and the range of input

values displayed. The Scope parameter values can be changed during a

simulation.

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Data type support

The Scope block accepts real (not complex) signals of any data type that

Simulink supports, including fixed-point and enumerated data types. The

Scope block also accepts homogeneous vectors.

4.2 Multiplexer

The Mux block combines its inputs into a single vector output. An input

can be a scalar or vector signal. All inputs must be of the same data type

and numeric type. The elements of the vector output signal take their

order from the top to bottom, or left to right, input port signals. The Mux

block accepts real or complex signals of any data type that Simulink

supports, including fixed-point and enumerated data types.

Figure 7: Function Block Parameters Editor of a Mux

4.3 Derivative Control

Derivative control is an embedded subsystem designed specifically for this

model. This system consists of following blocks:

Derivative Term

The derivative of the process error is calculated by determining the

slope of the error over time. Derivative action predicts system

behaviour and thus improves settling time and stability of the

system.

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Saturation Block

The Saturation block imposes upper and lower limits on an input

signal.

Figure 8: Saturation Block on Workspace

Its output varies as follows:

Table 5:

When the input is... Where... The block

output is

the...

Within the range specified by

the Lower limit and Upper

limit parameters

Lower limit ≤ Input value ≤

Upper limit

Input value

Less than the Lower limit

parameter

Input value < Lower limit Lower limit

Greater than the Upper limit

parameter

Input value > Upper limit Upper limit

4.4 Fuzzy Logic Toolbox

Fuzzy inference is a method that interprets the values in the input vector

and, based on user-defined rules, assigns values to the output vector.

Using the editors and viewers in the Fuzzy Logic Toolbox, you can build

the rules set, define the membership functions, and analyze the behaviour

of a fuzzy inference system (FIS). The following editors and viewers are

provided: Fuzzy Logic Toolbox provides functions, apps, and a

Simulink block for analyzing, designing, and simulating systems based on

fuzzy logic.

The main components of Fuzzy Logic Toolbox are:

FIS Editor

Displays general information about a fuzzy inference system

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Membership Function Editor

Lets you display and edit the membership functions associated

with the input and output variables of the FIS

Rule Editor

Lets you view and edit fuzzy rules using one of three formats: full

English-like syntax, concise symbolic notation, or an indexed

notation

Rule Viewer

Lets you view detailed behaviour of a FIS to help diagnose the

behaviour of specific rules or study the effect of changing input

variables

Surface Viewer

Generates a 3-D surface from two input variables and the output of

an FIS

Figure 9: The Membership Function Editor (top left), FIS Editor (centre), Rule

Editor (top right), Rule Viewer (bottom left), and Surface Viewer (bottom right).

4.5 Bus Creator

The Bus Creator block combines a set of signals into a bus. To bundle a

group of signals with a Bus Creator block, we set the block parameter

number of inputs to the number of signals in the group. The block displays

the number of ports that one specify. We connect to the resulting input

ports those signals that we want to group. The signals in the bus are

ordered from the top input port to the bottom input port. One can connect

any type of signal to the inputs, including other bus signals. To ungroup

the signals, we connect the output port of the block to a Bus Selector block

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port. One can use an array of buses as an input signal to a Bus Creator

block.

Figure 10: Block Parameters Editor of a Bus Creator

Naming Signals

The Bus Creator block assigns a name to each signal on the bus that it

creates. This allows you to refer to signals by name when you searching

for their sources or selecting signals for connection to other blocks.

The block offers two bus signal naming options. We can specify that:

Each signal on the bus inherits the name of the signal connected to

the bus. Inputs to a Bus Creator block must have unique names. If

there are duplicate names, the Bus Creator block appends (signal#)

to all input signal names, where # is the input port index.

Each input signal must have a specific name.

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To specify that bus signals inherit their names from input ports, we select

Inherit bus signal names from input ports from the list box on the Block

Parameters dialog box. The names of the inherited bus signals appear in

the Signals in the bus list box.

The Bus Creator block generates names for bus signals whose

corresponding inputs do not have names. The names are of the form signal

n, where n is the number of the port to which the input signal is

connected.

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CHAPTER 5

SIMULATION

5.1 Simulink Model and Fuzzy Surface

Figure 11: SIMULINK model of fuzzy control D.C. machine

Figure 12: FIS Editor

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Figure 13: Membership function for input variable “e”

Figure 14: Membership function for output variable “Controls”

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Figure 15: Membership function for input variable “de”

Figure 16: Rule Editor

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Figure 17: Rule Viewer

Figure 18: Surface View for Fuzzy Controller

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5.2 Simulation Results

Figure 19: Output of the system

Figure 20: Output of Fuzzy Controller

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CHAPTER 6

CONCLUSION

6.1 Conclusion This report presents and studies a straight-forward method of creating a

mathematical model which has been successfully applied to a variety of

membership functions. This new approach offers a key of advantage over

the traditional methods, which makes it suitable for several dc motor drive

applications. The project focused the attention to apply the smooth control

of speed in D.C. Machines up to the 95% and with minimization of speed

error. The simulation and experimental studies clearly indicate the

superiority of fuzzy control. It is well seen in the case of sudden change

due to load torque disturbances because it is inherently adaptive in

nature. The final experimental results clarify the success, the simplicity

and the generality of the design software controller. The extension of this

research is to apply the neural network techniques for the dc motor

applications.

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CHAPTER 7

BIBLIOGRAPHY

[1] Malhotra Rahul; 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.

[2] Ahmed, F.I.; Mahfouz, A.A.; Ibrahim, M.M.; "A novel fuzzy controller for DC

motor drives," Electrical Machines and Drives, 1999. Ninth International

Conference on (Conf. Publ. No. 468), vol., no., pp.325-328, 1999.

[3] Aydemir, S.; Sezen, S.; Ertunc, H.M.; "Fuzzy logic speed control of a DC

motor," Power Electronics and Motion Control Conference, 2004. IPEMC

2004. The 4th International, vol.2, no., pp.766-771 Vol.2, 14-16 Aug. 2004.

[4] Addasi Emad Said; “Modelling and Simulation of DC-Motor Electric Drive

Control System with Variable Moment of Inertia” ACEEE Int. J. on

Electrical and Power Engineering, Vol. 4, No. 1, Feb 2013.

[5] Chakravorty Jaydeep , Sharma Ruchika; “Fuzzy Logic Based Method of

Speed Control of DC Motor” International Journal of Emerging Technology

and Advanced Engineering, Volume 3, Issue 4, April 2013.

[6] Kumar Ravinder, Vineet Girdha; “High Performance Fuzzy Adaptive Control

for D.C. Motor” Global Journal of Researches in Engineering Electrical and

Electronics Engineering, Volume 13, Issue 10, Version 1.0, Year 2013.