IMPLEMENTATION OF FUZZY LOGIC CONTROLLER ON REVOLUTE

CONTROL UNIVERSAL STRETCH&BENDING MACHINE (USBM)

NINA NAISHA BINTI SUHAIMI

This thesis is submitted as partial fulfillment of the requirements for the award of the

Bachelor of Electrical Engineering (Hons.) (Electronics)

Faculty of Electrical & Electronics Engineering

Universiti Malaysia Pahang

APRIL, 2009

“I hereby acknowledge that the scope and quality of this thesis is qualified for the

award of the Bachelor Degree of Electrical Engineering (Electronics)”

Signature : ______________________________________________

Name : REZA EZUAN BIN SAMIN

Date : 27 APRIL 2009

DEDICATION

Special dedication to my parents, my fiancé, and family members that always

inspire, love and stand beside me, my supervisor, my beloved friends, my fellow

colleagues, and all faculty lecturers and members.

For your love, care, support and believe in me. Thank you so much.

God bless you all –Amin-

ACKNOWLEDGEMENT

Alhamdulillah, His Willingness has made it possible for the author to

complete the final year project in time.

I would like to take this opportunity to express gratitude to my dedicated

supervisor, Mr. Reza Ezuan bin Samin for guiding me this project at every stage with

clarity and that priceless gift of getting things done by sharing his valuable ideas as

well as share his knowledge. Not to forget, thanks also to Mr. Syahrulnaim bin

Mohamad Nawi, Mr. Nasrul bin Salim Pakheri, Mr. Muhammad Hamka bin Embong

and Mr. Azlan bin Sayuti for their meaningful criticism and guidance.

I would also like to thank to all UMP lecturers and electrical technicians

whom had helped directly or indirectly in what ever manner thus making this project

a reality.

Not forgotten are my best colleagues for their openhandedly and kindly

guided, assisted and supported and encourage me to make this project successful. My

heartfelt thanks to my parents Mr. Suhaimi bin Abdul Ghani, Madam Noraini binti

Abdul Ghaffar and Mr. Ahmed Faisal bin Mohammad Supian which always support

and prey on me throughout this project. Their blessing gave me the high-spirit and

strength to face any problem occurred and to overcome them rightly.

The great cooperation, kindheartedness and readiness to share worth

experiences that have been shown by them will be always appreciated and treasured

by me, thank you.

Thank You Nina Naisha binti Suhaimi

ABSTRACT

Motor speed control is very important in rotating machinery applications.

There are many applications that have been developed based on motor speed control

theory such as to run the machines at most factory automation industry as well

known the machines are easiest to damage without controller. The speed control of

motor is very difficult to be implemented by using conventional control techniques,

as it requires a very complex mathematical model. The purpose of this project is to

describe the research of fuzzy logic controller (FLC) design based on programmable

logic controller (PLC) in order to control the speed of the motor. The model of the

PLC that has been used in this project is OMRON CJIG-CPU42P where this PLC

has a build in loop control that can be made the ladder diagram quite simple using

function block in Cx-process tools. In this project, the system without controller

shows that is an open loop control. Therefore, when break is applied there is no

feedback for the system to increase the voltage in order for the motor to maintain the

desired speed output. Compare by using the controller FLC, when the breaking is

applied there is a feedback for the system to increase the voltage to get the desired

output that the user need. From this hardware implementation there are five rules that

have been used which is five membership functions with trapezoid and triangular

shape. Analysis will be done and it shows that the triangular shape is much better

compare to the trapezoid shape and without controller in the system. Before the

controller will be implementing in the PLC, the simulations were done using

MATLAB fuzzy logic toolbox and SIMULINK. The objective of the simulation is to

predict the system response of the motor in with or without controller.

ABSTRAK

Kawalan kelajuan motor adalah sangat penting dalam aplikasi jentera

berputar. Terdapat banyak aplikasi yang telah dibangunkan berdasarkan teori

kawalan kelajuan motor seperti menggerakkan mesin dikebanyakkan kilang industri

automasi, seperti yang diketahui mesin mudah mengalami kerosakkan tanpa

pengawal. Kawalan kelajuan motor adalah sukar untuk dilaksanakan dengan

mengunakan teknik konvensional, kerana ia memerlukan model matematik yang

kompleks. Tujuan projek ini dijalankan adalah untuk menerangkan kajian berkenaan

reka bentuk kawalan fuzzy logic (FLC) berdasarkan Programmable Logic Controller

(PLC) untuk mengawal kelajuan motor. Model PLC yang digunakan dalam projek

ini adalah OMRON CJ1G-CPU 42P yang mana PLC ini mempunyai kawalan

gelungan terbina dalaman dimana ia dapat meringkaskan ladder diagram dengan

menggunakan function block didalam perisian Cx-process.Dalam projek ini, sistem

tanpa pengawal menunjukkan ia adalah kawalan gelungan terbuka. Oleh itu, apabila

gangguan luar diberikan, tiada tindak balas terhadap sistem ini yang mana

membolehkan peningkatan voltan terhadap motor bagi mengekalkan keluaran

kelajuan yang diinginkan. Berbanding dengan menggunakan kawalan FLC, apabila

gangguan luar diberikan, terdapat tindak balas kepada sistem yang boleh

meningkatkan nilai voltan untuk mendapatkan keluaran kelajuan yang diinginkan

oleh pengguna. Daripada perlaksanaan perkakas ini, terdapat lima peraturan yang

digunakan iaitu fungsi keahlian dengan bentuk trapezoid dan segitiga. Analisis yang

telah dilakukan menujukkan bahawa bentuk segitiga adalah lebih baik berbanding

bentuk trapezoid dan tanpa pengawalan didalam sistem. Sebelum pelaksanaan

pekakas pengawalan dilakukan pada PLC, sistem ini disimulasikan dengan

menggunakan MATLAB Fuzzy Logic Toolbox dan SIMULINK. Objektif simulasi

ini adalah untuk meramalkan respon sistem motor bersama atau tanpa pengawal.

TABLE OF CONTENTS

CHAPTER TITLE

PAGE

DECLARATION i

DEDICATION ii

ACKNOWLEDGEMENT iii

ABSTRACT iv

ABSTRAK v

TABLE OF CONTENTS vi

LIST OF TABLE ix

LIST OF FIGURE x

LIST OF SYMBOLS xiii

LIST OF ABBREVIATIONS xiv

LIST OF EQUATIONS xv

LIST OF APPENDICES xvi

1 INTRODUCTION 1

1.1 Background 1

1.1.1 Introduction to the project 3

1.1.2 Problem Statement 4

1.1.3 Problem Solving 4

1.2 Objectives 5

1.3 Scopes of the project 5

2 LITERATURE REVIEW

7

2.1 Structure of Fuzzy Logic Controller 7

2.1.1 Preprocessing 8

2.1.2 Fuzzification 8

2.1.3 Rule Base Evaluation & Inference Engine 9

2.1.4 Deffuzification 11

2.1.4.1 Centre of Gravity (COG) 12

2.1.4.2 Centre of Gravity for Singletons(COGS) 12

2.1.4.3 Mean of Maxima (MOM) 13

2.1.4.4..Leftmost Max (LM), Rightmost Max (RM) 13

2.1.6 Postprocessing 13

2.2 Definition of Programmable Logic Controller (PLC) 13

2 2 1 Advantages using PLC 16

2.3 AC Motor 17

2.4 Encoder 18

2.5 High Speed Counter 19

2.6 Inverter 19

2.7 Relay 21

3 METHODOLOGY

22

3.1 Introduction 22

3.2 Flowchart for full project 24

3.2.1 Phase I : Project Preview 26

3.2.2. Phase II : MATLAB Simulation 26

3.2.2.1 Motor modeling without controller 27

3.2.2.2 Motor modeling with fuzzy logic controller 28

3.2.3 Phase III : Construct PLC Panel 30

3.2.4 Phase IV : Ladder Diagram 31

3.2.5 Phase V : Design Function Block 35

3.2.6 Phase VI : Hardware Construction 44

3.2.7 Phase VII : Hardware Integration with PLC 45

4 RESULT AND DISCUSSION

46

4.1 Simulation in MATLAB 46

4.1.1 Modeling of Motor 47

4.1.2 Motor Modeling without Controller 47

4.1.3 Motor Modeling With Fuzzy Logic Controller 49

4.2 Hardware Implementation 52

4.2.1 Hardware Implementation without Controller 53

4.2.2 Hardware Implementation with FLC 55

4.2.3 Comparisons of Speed Response 60

5 CONCLUSION AND RECOMMENDATION

62

5.1 Conclusion 62

5.2 Recommendation 63

5.3 Costing and Commercialization

64

REFERENCES 65

APPENDIX A : Derivative of Transfer Function

APPENDIX B : Calculation of the Power Consumption

APPENDIX C : Circuit Diagram of Panel PLC

APPENDIX D : Step to Creating the Function Block in Cx-Process

APPENDIX E: Connection between High Speed Counter and Encoder

APPENDIX F: Figure of Panel PLC and Hardware

APPENDIX G: Data Sheets

APPENDIX H: Data from Tuning Screen

LIST OF TABLES

TABLE NO TITLE

PAGE

2.1 Advantages of PLC 16

3.1 Actual motor parameters 27

4.1 Performance Comparisons Response of Ac Motor 60

LIST OF FIGURES

FIGURE NO TITLE

PAGE

1.1 Block Diagram Implementation of Fuzzy Logic

Controller

4

2.1 Block Diagram of Fuzzy Controller 8

2.2 Fuzzifiction 8

2.3 Mamdani-style rule evaluation (Simulation) 9

2.4 Sugeno-style rule evaluation (Hardware Implementation) 10

2.5 Mamdani-style aggregation of the rule outputs 11

2.6 Sugeno-style aggregation of the rule outputs 11

2.7 Features of PLC CJ1G based Process Control 15

2.8 PLC CJ1G-CPU42P 15

2.9 AC Motor 17

2.10 Per-phase approximate equivalents circuit of an induction

motor

18

2.11 Encoder and dimension drawings. 18

2.12 Dimension drawings of High Speed Counter 19

2.13 Terminal Connection Diagram 20

2.14 Relay OMRON MK2P-I 21

3.0 Design without Controller 23

3.1 Design for Fuzzy Logic Controller 23

3.2 Flow Chart of the full Project 25

3.3 Block Diagram without Controller 27

3.4 Block Diagram with Fuzzy Logic Controller 28

3.5 FIS Editor 29

3.6 Membership Function Editor 29

3.7 Rule Editor 30

3.8 Running Motor 32

3.9 Conversion of Pulse to Speed 33

3.10 PLC IO Table 34

3.11 Setup Analog Output 34

3.12 Figure to active analog output using MOV block 35

3.13 Function Block Diagram without Controller 36

3.14 Function Block Diagram without Controller 36

3.15 Setting for 2pos. ON/OFF Block 37

3.16 Setting for DA 08V/C Block 37

3.17 Setting for User Link Table Block 38

3.18 Flow Chart of FLC Design Methodology 39

3.19 Fuzzy Logic Controller Function Block Diagram 40

3.20 Setting for Constant Selector 40

3.21 Setting for Fuzzification range for trapezoidal shape 41

3.22 Range Fuzzification for trapezoidal shape 41

3.23 Fuzzification range for triangular shape 42

3.24 Range Fuzzification for triangular shape 42

3.25 Setting for rule base and output 43

3.26 Setting for User link Table 43

3.27 Connection between the pins 44

3.28 PLC Design Methodology 45

4.1 Programming in the M-file 47

4.2 Motor Modeling without Controller 48

4.3 Speed Response Motor without Controller 49

4.4 Motor Modeling with Fuzzy Logic Controller 50

4.5 Speed Response with FLC using Triangular Shape 51

4.6 Speed Response with FLC using Trapezoidal Shape 52

4.7 Tuning Screen without Controller 53

4.8 Graph without Controller 54

4.9 Response of AC Motor using FLC with Trapezoidal

Shape

55

4.10 Response of Ac Motor using FLC with Triangular Shape 55

4.11

Response of Ac Motor using FLC with Trapezoid

Shape

56

4.12

Response of Ac Motor using FLC with Triangular

Shape

57

4.13 Range for Triangular Shape 58

4.14 Rule Base 59

4.15 Aggregation of rules output 59

LIST OF SYMBOLS

Jm - Equivalent Inertia by the Motor

Dm - Equivalent Viscous Density by the Motor

Kt, - Motor Torque Constant

Kb - Back emf Constant

Ra - Armature Resistance

La - Armature Inductance

V

Ia

- Voltage

- Armature Current

θ - Rotating Speed

τ - Torque

PT - Total Power

dE - Del error

LIST OF ABBREVIATIONS

PLC Programmable Logic Controller

FLC Fuzzy Logic Controller

SP Set Point

MV Manipulated Variable

PV Process Variable

PWM Pulse Width Modulation

LIST OF EQUATIONS

1 - Center of Gravity (COG) Equation

2 - Centre of gravity methods for singletons (COGs)

equation

3 - Conversion of Pulse to Speed Equation

4 - Derivation of Transfer Function Equation

5 - Overshoot Percentage

6 - Rise Time

LIST OF APPENDICES

APPENDIX TITLE

A Derivative of Transfer Function

B Calculation of the Power Consumption

C Circuit Diagram of Panel PLC

D Step to Creating the Function Block in Cx-Process

E Connection between High Speed Counter and Encoder

F Figure of Panel PLC and Hardware

G Data Sheets

CHAPTER 1

INTRODUCTION

This chapter explains the background and the introduction of this overall

project which includes the introduction of project, problem statement, problem

solving, objectives and scope project.

1.1 Background

In the industrial automation there are many types of machines that are used

for production such as stretch and bending machines. These machines can work more

efficiently if both of them are combined together. Currently, Lewa Attendorn

Company has employed a panel PC and soft-PLC to conveniently teach its universal

sheet metal and profile stretch-bending machines complex three dimensional

program with 12 to 16 axes. The PLC functionality of the entire machine is handled

by a Simatic WinAC RTX soft-PLC. This is a very fast solution without

communication slowing hardware between the HMI system and the programmable

logic control (PLC). Their universality expands the field of the stretch-bending

machine by Lewa Attendorn for beyond the automotive realm.[1]

Stretch machines are an engineered for all purpose of applications and

suitable for stretch bundling application. Normally, it is used in industries such as for

wrapping, packaging and tapping products. Packaging machinery usually uses to

package products or components including equipment that forms, fills, seals, wraps,

cleans and packages at a different levels of automation.

Bending machines have a variety of functions and specifications which are

normally use to bend and fold metal by pressing it into a die. There are several types

of press brakes and bending machines such as a hydraulic press brake, folding

equipment, bending machine, press brake tooling, CNC brake press, and a sheet

metal press brake. It is used in many industries including automotive and aircraft

industries where the metal parts are a constant need. The controller is much

important to make sure the stretch and bending machine are smoothly and reliable

functionality in industrial automation.

Fuzzy logic control is an effective approach for systems which are difficult

to model. This controller is suitable for stretch and bending machine where the fuzzy

logic control methods a rather new approach to the problems of controlling complex

nonlinear systems, the systems whose mathematical model is difficult or impossible

to describe, and the systems with multiple inputs and outputs characterized by hardly

defined internal interference. It must be said that fuzzy logic control techniques

earned respect from the engineering population after numerous applications on

technical and non-technical systems, especially complex systems in industry,

economy, and medicine.

Fuzzy logic and the theory of fuzzy sets are the result of a broader

comprehension of practical control problems and control actions, performed by

human operators, which could not have been correctly interpreted by using classical

bivalent logic and conventional methods of automatic control. In the beginning of his

globally successful professional career “the father of fuzzy logic,” Professor Lotfi

A.Zadeh, affiliated with the University of California at Barkeley, USA, realized that

the existing control theory was very limited and that it did not provide real solutions

for the abovementioned classes of the systems. In the 1960s Professor Zadeh made

an ingenious shift from standard thinking and interpretation and created the

fundamentals of a new control theory, which got full recognition and obtained

numerous followers, after almost 20 years of struggle with fuzzy control

opponent.[2]

1.1.1 Introduction to the project

Nowadays, most of factory automation industry used machines where most of

the machines are easier to damage without the controller. The question is, how to

control the machine to ensure that it is economic and reliability system using in

industry plant.

In developing of this project, a simulation of the revolute control of a motor

using Fuzzy Logic Control (FLC) will be done in MATLAB environment. The Fuzzy

Logic Controller designed in this study applies the required control voltage based on

motor speed. The simulation results show that the control with Fuzzy Logic

Controller (FLC) can improve in terms of percentage overshoot and steady state

error.

Programmable Logic Controller (PLC) ladder diagram programming will be

constructed with fuzzy logic control (FLC) implementation then construct the

hardware of revolute control USBM. To make the PLC ladder diagram would be

simpler the PLC Omron CJ1G will be used where the loop process is build in up and

using the function block in Cx-Process tools it can make the ladder diagram simpler.

1.1.2 Problem Statement

Generally, machine in factory are easily damage without implementation of

revolute control in it system. The desired performance characteristics of control

system are specified in term of the transient response. The transient response of a

practical control system usually exhibits damped oscillation before reaching steady

state. For example, as a machine it having a high overshoot is an undesired condition

since the motor starting current is very high.

1.1.3 Problem Solving

To solve the problem statement, control methodology such as a fuzzy logic

controller is used to limit the overshoot as well to reduce the starting motor current of

the machine. The Fuzzy Logic Controller is chosen to interface with the motor

because it is suitable for application which has nonlinearities such as speed of the

motor. The figure below shows the block diagram of an USBM (motor) with

implementation of Fuzzy Logic Controller.

Figure 1.1: Implementation of Fuzzy Logic Controller

1.2 Objective

The overall aim of the whole project is to control the motor speed in USBM

using Programmable Logic Controller (PLC) and to design the Fuzzy Logic

Controller (FLC) in the Programmable Logic Controller (PLC) for better performance

system of the revolute control USBM.

1.3 Scopes of Project

This project is to design a fuzzy logic controller that can be use to control the

speed of a motor. As a machine performance is a vital factor for a big production

line, this project will examine the efficiency and performance of a motor with

implementation of control methodology. Thus, the focuses of this project are stated

below:-

i. MATLAB simulation of speed control motor using Fuzzy Logic Toolbox.

Comparisons of simulation performance of uncontrolled and controlled

speed are examined.

ii. Design, construct, wiring Panel PLC and Configure I/O card of PLC

CJ1G-CPU42P.

iii. Construct the hardware of revolute control USBM. Consist of motor,

inverter, relay and encoder.

iv. Studies of PLC Programming consist of Cx-Programmer (Version 7.2 )

and Cx-Process (Version 5.1)

v. Design PLC ladder diagram programming + function block in Cx-process

tools with Fuzzy Logic Controller implementation.

CHAPTER 2

LITERATURE REVIEWS

This chapter focused on the literature review for each component in this

project. All the component is describe in details based on the finding during

completion of this project. This chapter review about the structure of Fuzzy Logic

Controller (Mamdani Style and Sugeno Style), Programmable Logic Controller

(PLC), Ac Motor, Encoder, High Speed Counter, Inverter and Relay.

2.1 Structure of Fuzzy Logic Controller

There are specific components characteristic of a fuzzy controller to support a

design procedure. In the block diagram in Figure 2.1, the controller is between a

preprocessing block and a post-processing block. The following explains the diagram

block by block. [3]

Figure 2.1: Blocks of Fuzzy Controller

2.1.1 Preprocessing

The inputs are most often hard or crisp measurement from some measuring

equipment rather than linguistic. A preprocessor, the first block in Figure 2 shows the

conditions the measurements before enter the controller.[3]

2.1.2 Fuzzification

The first block inside the controller is fuzzification as shown in Figure 2.2, the

first step is to take the crisp inputs, x1 and y1 (project funding and project staffing),

and determine the degree to which these inputs belong to each of the appropriate

fuzzy sets.[4]

Figure 2.2: Fuzzification

2.1.3 Rule Base Evaluation & Inference Engine

The second step is to take the fuzzified input, (x=A1) = 0.5, (x=A2) = 0.2,

(y=B1) = 0.1 and (y=B2) = 0.7, and apply them to the antecedents of the fuzzy

rules. If a given fuzzy rule has multiple antecedents, the fuzzy operator (AND or OR)

is used to obtain a single number that represents the result of the antecedent

evaluation. [4]

Crisp Input

y1

0.1

0.7

1

0y1

B1 B2

Y

Crisp Input

0.2

0.5

1

0

A1 A2 A3

x1

x1 X

(x = A1) = 0.5

(x = A2) = 0.2

(y = B1) = 0.1

(y = B2) = 0.7

To evaluate the disjunction of the rule antecedents, the OR fuzzy operation

will use. Typically, fuzzy expert systems make use of the classical fuzzy operation

union:

A B(x) = max [ A(x), B(x)]

Similarly, in order to evaluate the conjunction of the rule antecedents,

the AND fuzzy operation intersection will apply:

A B(x) = min [ A(x), B(x)

Figure 2.3: Mamdani-style rule evaluation (Simulation)

Sugeno-style fuzzy inference is very similar to the Mamdani method. Sugeno

changed only a rule consequent. Instead of a fuzzy set, he used a mathematical

function of the input variable. The format of the Sugeno-style fuzzy rule is:

IF x is AND y is B THEN z is f (x, y). [4]

Where x, y and z are linguistic variables A and B is fuzzy sets on universe of

discourses X and Y, respectively; and f (x, y) is a mathematical function.

The most commonly used zero-order Sugeno fuzzy model applies fuzzy rules

in the following form:

A3

1

0 X

1

y1 0 Y

0.0

x1 0

0.1 C1

1

C2

Z

1

0 X

0.2

0

0.2 C1

1

C2

Z

A2

x1

Rule 3:

A1 1

0 X 0

1

Z x1

THEN

C1 C2

1

y1

B2

0 Y

0.7

B1 0.1

C3

C3

C3 0.5 0.5

OR (max)

AND (min)

OR THEN Rule 1:

AND THEN Rule 2:

IF x is A3 (0.0) y is B1 (0.1) z is C1 (0.1)

IF x is A2 (0.2) y is B2 (0.7) z is C2 (0.2)

IF x is A1 (0.5) z is C3 (0.5)