vii
TABLE OF CONTENTS
CHAPTER
1
2
TITLE
PAGE
DECLARATION OF THESIS
ii
DEDICATION
iii
ACKNOWLEDGEMENT
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENTS
vii
LIST OF TABLES
xi
LIST OF FIGURES
xii
LIST OF SYMBOLS AND ABBREVIATIONS
xvi
LIST OF AXPENDICES
xvii
INTRODUCTION
1.1
Overview of the project
1
1.2
Problem Statements
4
1.3
Objectives
4
1.4
Scope of Works
5
LITERATURE REVIEW
2.1
Introduction
6
2.2
Terminology: Viscosity
6
2.3
Earlier Model (Prototype) of Intelligent
8
Pneumatic Actuator
viii
2.4
Control Algorithm based on the Previous
9
Experimental Setup
2.5
Fuzzy Logic Speed Control of an Inductor
11
Motor
2.6
Fuzzy Control of the Compressor Speed in a
12
Refrigeration Plant
2.7
Fuzzy and Neural Controllers for a Pneumatic
13
Actuator
2.8
Speed Control of Separately Excited DC Motor
14
using Self Tuned Fuzzy PID Controller
2.9
A Comparative Analysis of PI, Fuzzy Logic
15
and ANFIS Speed Control of Permanent
Magnet Synchronous Motor
2.10
Non-linear Modeling and Cascade Control of
16
an Industrial Pneumatic Actuator System
2.11
3
Summary
17
METHODOLOGY
3.1
Overview
18
3.2
Flow Chart of Methodology
19
3.3
Modeling and Control Approaches
20
3.4
Structure of Intelligent Pneumatic Actuator
21
3.5
Operation of the IPA System
22
3.6
Nonlinear Mathematical Modeling
24
3.6.1 Piston-Load Dynamic Model
25
3.6.2 Modeling of the Cylinder Chambers
27
3.6.3 Valve Model
28
3.7
Development of the IPA Simulation Model
30
3.7.1 Position Control (Closed-loop)
31
3.7.2 Viscosity Control (Closed-loop)
31
3.8
Controller Design
3.8.1 Fuzzy Logic Controller
3.8.1.1 Fuzzy Logic Controller Design
32
33
37
ix
3.8.2 Adaptive Neuro-Fuzzy Inference System
40
(ANFIS) Controller
3.8.2.1 ANFIS Controller Design
3.8.3 ANFIS Cascade Controller
47
3.8.4 Self-tuning Fuzzy PI Controller
48
3.8.4.1 Self-tuning Fuzzy PI Controller Design
4
44
49
3.9
Linearization
51
3.10
Summary
54
RESULTS AND DISCUSSIONS
4.1
Introduction
55
4.2
Open-loop Response of Nonlinear
56
Mathematical Model
4.3
Closed-loop Response of Nonlinear
58
Mathematical Model
4.3.1 Position Control (Nonlinear Mathematical
58
Model)
4.3.1.1 Conventional PI Controller (Position
59
Control)
4.3.1.2 Fuzzy Logic Controller (Position
62
Control)
4.3.1.3 ANFIS Controller (Position Control)
64
4.3.1.4 ANFIS Cascade Controller (Position
65
Control)
4.3.1.5 Self-tuning Fuzzy PI Controller
68
(Position Control)
4.3.1.6 Comparison of Controllers applied for
70
Position Control
4.3.2 Viscosity Control (Nonlinear Mathematical
71
Model)
4.4
Linearization
4.4.1 Linear Differential Equation (Taylor Series
Expansion)
76
76
x
4.4.2 Linear Transfer Function (Control and
81
Estimation Tools Manager)
5
REFERENCES
Appendices A - B
4.5
3D Animation for IPA System
85
4.6
Summary
86
CONCLUSION AND FUTURE WORK
5.1
Conclusion
87
5.2
Future Work
89
90
94 - 101
xi
LIST OF TABLES
TABLE NO.
TITLE
PAGE
2.1
Specifications of the prototype of intelligent cylinder
9
2.2
Specification of parameter settings for each experiment
10
3.1
Important parameters of the IPA system
24
3.2
Upstream and downstream pressures according to the process
29
of cylinder chamber
3.3
Criteria for designing fuzzy logic controller for IPA system
39
3.4
Fuzzy rules matrix for fuzzy controller design
40
3.5
ANFIS editor training criteria
46
3.6
Fuzzy rule matrix of the fuzzy PI controller
51
4.1
Performance of step response using conventional PI controller
60
4.2
Performance of step response using fuzzy logic controller
63
4.3
Performance of step response using ANFIS controller
64
4.4
Performance of step response using ANFIS cascade controller
66
4.5
Performance of step response using self-tuning fuzzy PI
69
controller
4.6
Performances of step response using different kinds of
71
controllers
4.7
Comparison of RMSE (m) values for sine wave tracking
71
4.8
Output forces (N) for different value of velocity and viscosity
75
parameter
xii
LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
1.1
Basic structure of a pneumatic actuator
2
1.2
New LA36 intelligent pneumatic actuator from LINAK
3
1.3
Intelligent pneumatic actuator
3
2.1
A mass-spring-damper system
7
2.2
Damping effect when external force is being applied to an
8
object
2.3
Comparison between Intelligent cylinder and commercial
8
cylinder
2.4
Structure of previous experiment setup
9
2.5
A unified control block diagram
10
2.6
Control scheme for an induction motor voltage-source inverter 11
drive
2.7
Fuzzy controller block diagram
12
2.8
Vapor compression experimental plant
12
2.9
Comparison of energy consumption using both the fuzzy
13
control and thermostatic control
2.10
Fuzzy force - position feedback system
14
2.11
Separately excited DC motor model
14
2.12
Speed vs time response of fuzzy tuned PID controlled DC
15
motor
2.13
d-axis equivalent circuits of the sinusoidal PMSM brushless
15
machine
2.14
Step response for fuzzy logic and ANFIS controller
16
xiii
2.15
Closed loop system with cascade controller
17
3.1
Flow chart for the modeling and control approaches
19
3.2
Organization K-chart for whole project
20
3.3
Structure of new intelligent pneumatic actuator
21
3.4
Simplify diagram of the IPA structure
22
3.5
Schematic diagram and valve connection of the intelligent
23
pneumatic actuator
3.6
Translational mechanical systems and their relationships
25
3.7
Model of IPA plant
30
3.8
Position control simulation model
31
3.9
Viscosity control simulation model
32
3.10
Components of the fuzzy logic controller
34
3.11
Process of fuzzification
35
3.12
Computation of the centroid defuzzification method
36
3.13
Operations of fuzzy controller
37
3.14
Flow chart of the design procedure of a fuzzy logic control
37
system
3.15
MATLAB graphical tool for designing fuzzy logic controller
38
3.16
Membership function of error (e)
39
3.17
Membership function of control signal (u)
39
3.18
A typical architecture of an ANFIS model
41
3.19
Flow chart of training procedure of ANFIS
43
3.20
Graphical user interface of ANFIS editor
44
3.21
Collection of input output training data
45
3.22
Input and output training data for ANFIS system
45
3.23
Result from training data
46
3.24
Generated membership functions for input variable through
46
ANFIS editor
3.25
Simulation model using ANFIS cascade controller
48
3.26
Structure of self-tuning fuzzy PI controller
49
3.27
Structure of fuzzy logic for fuzzy PI controller
50
3.28
Membership function of input
50
3.29
Membership function of input ∆
50
xiv
3.30
Membership function of output
50
3.31
Membership function of input
50
3.32
Some examples of graph showing nonlinearities problem
52
3.33
Graphs showing comparison of (a) linear equation and (b)
53
nonlinear equation
3.34
GUI of Control and Estimation Tools Manager
54
4.1
Simulink block diagram for nonlinear mathematical IPA
56
model
4.2
Open-loop step response
57
4.3
Open-loop sine wave response
57
4.4
Simulink block diagram for position control
58
4.5
Step response for position tracking using conventional PI
59
controller
4.6
Multistep response for position tracking using conventional PI
59
controller
4.7
Sine wave response for position tracking using conventional
60
PI controller
4.8
Input and piston speed response
61
4.9
Change of pressure of chamber 1 versus time
61
4.10
Mass flow rate versus time
62
4.11
The structure of the fuzzy logic controller
62
4.12
Step response for position tracking using fuzzy logic
63
controller
4.13
Step response for position tracking using ANFIS controller
64
4.14
Simulink block diagram of closed-loop system using ANFIS
65
cascade controller
4.15
Step response for position tracking using ANFIS cascade
65
controller
4.16
Multistep response for position tracking using ANFIS cascade
66
controller
4.17
Square wave response for position tracking using ANFIS
cascade controller
67
xv
4.18
Sine wave response for position tracking using ANFIS
67
cascade controller
4.19
Simulink block diagram of fuzzy PI controller
68
4.20
Tuning process of PI controller’s parameters
68
4.21
Step response for position tracking using self-tuning fuzzy PI
69
controller
4.22
Step responses for position tracking using different types of
70
controllers
4.23
Simulink block diagram for the viscosity control
72
4.24
Sinusoidal wave responses for force tracking
73
4.25
Force reference with output force versus time, based on four
74
different value of viscosity coefficient
4.26
Force reference with output force versus time, based on four
74
different value of velocity
4.27
Viscosity plot for intelligent pneumatic actuator
75
4.28
Simulation result of the position control using conventional PI
77
controller
4.29
Simulation graph for spool displacement form time 0 s to
78
0.377 s
4.30
Position tracking of 0.1 m step responses for the linear and
80
nonlinear system
4.31
Steps in linearizing the nonlinear system
81
4.32
Pole-zero map of the linear transfer function
82
4.33
Bode diagram of the linear transfer function
83
4.34
Simulink block diagram for position control using linear
83
transfer function
4.35
Position tracking of 0.1m step responses for the linear systems 84
(mathematical model and transfer function)
4.36
Complete 3D animation for the IPA system
85
xvi
LIST OF SYMBOLS AND ABBREVIATIONS
3D
-
Three dimension
ANFIS
-
Adaptive neuro-fuzzy inference system
CAD
-
Computer-aided design
CAM
-
Computer-aided manufacturing
DC
-
FIS
-
Direct current
Fuzzy inference system
FLC
-
Fuzzy logic controller
GUI
-
Graphical user interface
IC
-
Integrated circuit
IPA
-
Intelligent pneumatic actuator
LED
-
Light emitting diode
MIMO
-
Multi input multi output
MISO
-
Multi input single output
PASS
-
Pneumatic Actuator Seating System
PI
-
Proportional-integral
PSoC
-
Programmable system on chip
PSPM
-
Permanent magnet synchronous motor
PWM
-
Pulse-width modulation
RMSE
-
Root mean square error
SI
-
System identification
VRML
-
Virtual reality modeling language
xvii
LIST OF APPENDICES
APPENDIX
A
TITLE
Gantt chart for MEM 1813 – Research Project
PAGE
94
Proposal
Gantt chart for MEM 1825 – Master Project
94
B1
IPA complete parameter
95
B2
Complete Simulink block diagram for position
96
control (conventional PI)
B3
Complete Simulink block diagram for viscosity
97
control (conventional PI)
B4
Complete Simulink block diagram + Simulink 3D
98
Animation™
B5
Design of IPA 3D Model using V-realm Builder 2.0
99
B6
MATLAB coding for Linearization using Taylor series
100
expansion