vi
TABLE OF CONTENTS
CHAPTER
1
2
TITLE
PAGE
DECLARATION
ii
ACKNOWLEDGEMENT
iii
ABSTRACT
iv
ABSTRAK
v
TABLE OF CONTENTS
vi
LIST OF TABLES
ix
LIST OF FIGURES
x
LIST OF APPENDICES
xii
INTRODUCTION
1
1.1
Background of Study
1
1.2
Problem Statement
2
1.3
Objectives
2
1.4
Project Scopes
3
1.5
Project Outline
3
LITERATURE REVIEW
5
2.1
Introduction
5
2.2
Multivariable PID
5
2.3.
Multivariable PID Tuning Method
8
vii
2.4.
Optimization Technique
2.5.
Genetic Algorithm and Particle Swarm
Optimization
2.6.
3
14
17
3.1
Introduction
17
3.2
Flow of the study
17
3.3
Wastewater Treatment Plant
20
3.4
Multivariable PID Tuning
24
3.4.1
Davison method
25
3.4.2
Penttinen-Koivo method
25
3.4.3
Maciejowski method
26
3.4.4
Proposed Combined method
27
Optimization Technique
27
3.5.1
Particle Swarm Optimization
28
3.5.2
Genetic Algorithm
31
3.6
Objective Function
37
3.7
Simulation
38
RESULT AND DISCUSSIONS
40
4.1.
Introduction
40
4.2.
Open Loop Response
41
4.3.
PSO and GA Search
43
4.4.
Comparison between PSO and GA using Davison
44
4.5.
Results of MPID tuning using PSO
46
4.6.
Results of MPID tuning using GA
50
4.7
Results of best MPID tuning using PSO and best
MPID using GA
5
13
METHODOLOGY
3.5
4
Performance index
10
CONCLUSION AND FUTURE WORKS
54
57
viii
5.1
Introduction
57
5.2
Conclusion
57
5.3
Future Works
59
REFERENCES
60
Appendices
65
ix
LIST OF TABLES
TABLE NO.
TITLE
PAGE
3.1
Initial condition value
22
3.2
Kinetic parameter value
22
3.3
Parameter initialization in PSO algorithm
31
3.4
GA initialization
35
4.1
Open loop system performance data
42
4.2
MPID tuning parameter data using PSO
49
4.3
MPID tuning parameter data using GA
54
4.4
Best MPID tuning data
55
x
LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
2.1
Multivariable system
7
2.2
The PID tuning method classification
10
2.3
Optimization tools and techniques
11
2.4
Meta-heuristic algorithms for optimization
12
3.1
Methodology flowchart
19
3.2
Diagram of PID controller with optimization technique
19
3.3
Activated Sludge Process
20
3.4
Multivariable PID control system
25
3.5
PSO flowchart
29
3.6
GA flowchart
33
3.7
Nonlinear model of Simulink
39
4.1
Open loop step response of WWTP
41
4.2
Open loop bode diagram of WWTP
42
4.3
Graph of Fitness function versus iteration (PSO)
43
4.4
Graph of Fitness function versus iteration (GA)
43
4.5
Graph of fitness function versus number of particle/populations
45
4.6
Graph of computational time versus number of
45
particle/population
4.7
MPID output 1 response with PSO as parameter tuning (G11)
46
4.8
MPID output 2 response with PSO as parameter tuning (G22)
47
4.9
MPID system interaction due to input 2 using PSO as
47
parameter tuning (G12)
xi
4.10
MPID system interaction due to input 2 using PSO as
48
parameter tuning (G21)
4.11
MPID system responses using PSO as parameter tuning
48
4.12
MPID output 1 responses with GA as parameter tuning (G11)
51
4.13
MPID output 2 responses with GA as parameter tuning (G22)
51
4.14
MPID system interaction due to input 2 using GA as parameter
52
tuning (G12)
4.15
MPID system interaction due to input 1 using GA as parameter
52
tuning (G21)
4.16
MPID system response using GA as parameter tuning
53
4.17
Output response 1 using Proposed-Combined method
55
4.18
Output response 2 using Proposed-Combined method
56
xii
LIST OF APPENDICES
APPENDIX
TITLE
PAGE
A
Particle Swarm Optimization (PSO) MATLAB coding
65
B
Genetic Algorithm (GA) MATLAB coding
69
C1
Davison method performance index MATLAB coding
76
C2
Penttinen-Koivo method performance index MATLAB
coding
C3
Maciejowski method performance index MATLAB
coding
C4
Proposed Combined method performance index
MATLAB coding
77
79
81
D
Wastewater Treatment Plant MATLAB coding
83
E
MPID tuning parameter data
88
F
MPID controller Simulink block diagram
96
G
Gantt chart
98