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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)