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TABLE OF CONTENTS
CHAPTER
TITLE
PAGE
DECLARATION
DEDICATION
ACKNOWLEDGEMENT
ABSTRACT
ABSTRAK
TABLE OF CONTENTS
LIST OF TABLES
LIST OF FIGURES
LIST OF ABBREVIATIONS
LIST OF SYMBOLS
LIST OF APPENDICES
ii
iii
iv
v
vi
vii
xi
xii
xiv
xv
xvi
1
INTRODUCTION
1.1
Problem Statement
1.2
Objective of Research
1.3
Scope of Research
1.4
Thesis Outline
1
2
2
3
4
2
LITERATURE REVIEW
2.1
Introduction
2.2
Multi-objective Optimization
2.3
Terminology of Surrogate Modeling
2.4
Surrogate Modeling in Engineering Design Optimization
2.5
Surrogate Modeling Technique
2.6
Surrogate Modeling in Control System
2.7
Surrogate Modeling Design Optimization Strategies
2.8
Multi-objective Optimization Using Surrogate
Modeling
5
5
5
8
9
11
12
13
14
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2.9
3
4
5
Summary
MULTI-OBJECTIVE SURROGATE MODELING OPTIMIZATION ALGORITHM
3.1
Introduction
3.2
General Algorithm
3.3
Input Design Space
3.4
Design of Experiment - Latin Hypercube
3.5
Radial Basis Function Neural Network (RBFNN)
3.6
Objective Function - Integral Square Error (ISE)
3.6.1
Best Compromise
3.7
Summary
CASE STUDY 1: FORCED CIRCULATION EVAPORATOR
4.1
Introduction
4.2
Force Circular Evaporator
4.3
Experimental and Simulation Setup
4.4
Optimization Setup
4.4.1
Artificial Neural Network Setting
4.4.1.1 Radial Basis Function Neural
Network Setting
4.4.1.2 Feed Forward Neural Network
Setting
4.5
Results
4.5.1
Surrogate modeling versus Brute-force
search
4.5.2
Comparison with different approximation
model
4.5.3
Comparison with Non-dominated Sorting
Genetic Algorithm-II (NSGA-II) and
Strength Pareto Evolutionary Algorithm 2
(SPEA2)
4.6
Summary
CASE STUDY 2: REMOTELY OPERATED VEHICLE
5.1
Introduction
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16
16
16
18
20
21
24
25
25
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26
26
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33
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35
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5.2
5.3
5.4
5.5
5.6
6
Case Study - Nonlinear Model of the Underactuate
ROV
5.2.1
Body-Fixed Model of the Underactuated
ROV
5.2.2
An Earth-Fixed Model of the Underactuated ROV
5.2.3
Thruster Model
Optimization Setup
Results
5.4.1
Surrogate versus brute force search
5.4.2
Comparison with different approximation
model
5.4.3
Comparison with Non-dominated Sorting
Genetic Algorithm-II (NSGA-II) and
Strength Pareto Evolutionary Algorithm 2
(SPEA2)
Overall Comparison of the Controllers Performance
based On Best Compromise
Summary
THE MOSMO TOOLBOX
6.1
Introduction
6.2
Development goals
6.3
Toolbox Overview
6.3.1
Toolbox Limitation
6.4
Toolbox Algorithm and Organization
6.5
Toolbox Overview
6.5.1
Files
6.5.2
Installation
R
6.5.3
Setting up Simulink
File for MOSMO
Toolbox
6.6
Using Surrogate Modeling Optimization Toolbox
6.6.1
Defining the Problem
6.6.2
Defining the Design Space
6.6.3
Running the algorithm
6.6.4
Sampling Strategy
6.6.5
Surrogate Model
6.6.6
Plot and Display
6.7
Summary
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47
48
48
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51
53
54
56
60
61
61
61
62
64
64
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67
68
69
70
71
73
73
75
75
76
78
x
7
CONCLUSIONS AND FUTURE WORKS
7.1
Conclusions
7.2
Suggestion for Future Works
REFERENCES
Appendices A – B
79
80
81
82
89 – 90
xi
LIST OF TABLES
TABLE NO.
3.1
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
6.1
TITLE
Example of design space using a grid sequence
Constant value and description
Variables and steady state values
Variable constrain
Design space configuration for evaporator control system
Design space for evaporator optimization
Summary of PID evaporator optimization simulation result
NSGA-II and SPEA2 basic parameters setting
Comparison MOSMO with NSGA-II and SPEA2
Design space configuration for ROV control system
Parameters gains of PD input
Surrogate models setting
Summary of ROV optimization simulation result
NSGA-II and SPEA2 basic parameters setting
ROV comparison summary
Comparison of surge (x) response’s characteristics
Comparison of sway (y) response’s characteristics
Comparison of heave (z) response’s characteristics
MOSMO Toolbox sampling options
PAGE
19
28
29
31
32
33
39
40
41
49
50
51
54
54
56
57
58
59
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LIST OF FIGURES
FIGURE NO.
2.1
2.2
2.3
2.4
3.1
3.2
3.3
3.4
4.1
4.2
4.3
4.4
4.5
4.6
4.7
5.1
5.2
5.3
5.4
5.5
5.6
5.7
TITLE
Illustration of non-dominated solutions
Close-up view non-dominated solutions
Metamodeling hierarchy
Surrogate modeling design optimization strategies
Flowchart of multi-objective surrogate modeling optimization
Difference between random and grid design space
Example of Latin Hypercube sampling and filling
An RBF Neural Network
Force Circulation Evaporator.
R
Simulink
block diagram of force circular evaporator of PID
controller
Pareto-front of objective functions obtained by MOSMO and
actual simulation using Brute-force search
Pareto-fronts of MOSMO-RBFNN and MOSMO-FFNN with
actual Pareto-front of Brute-force search
Comparison MOSMO. NSGA-II and SPEA2.
Close-up view of best compromise MOSMO, NSGA-II and
SPEA2.
Respond of an evaporator using parameter gains obtained
from MOSMO, NSGA-II and SPEA2
Advance two coordinates frame of the ROV.
R
Simulink
block diagram of ROV PD controller.
Pareto-front of objective functions obtained by MOSMO and
actual simulation using Brute-force search
Pareto-fronts of different approximation model compare with
actual Pareto-front of from Brute force search
Comparison MOSMO. NSGA-II and SPEA2.
Best compromise MOSMO. NSGA-II and SPEA2.
Respond of surge (x) position using parameter gains obtained
from MOSMO, NSGA-II and SPEA2
PAGE
6
7
8
14
17
18
21
21
27
27
36
38
40
41
43
46
49
52
53
55
56
57
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5.8
5.9
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
6.10
6.11
6.12
6.13
6.14
6.15
6.16
6.17
6.18
6.19
6.20
Respond of sway (y) position using parameter gains obtained
from MOSMO, NSGA-II and SPEA2
Respond of heave (z) position using parameter gains obtained
from MOSMO, NSGA-II and SPEA2
MOSMO Toolbox interface
Flowchart MOSMO operation
Flowchart for loop inside MOSMO Toolbox
Sequence diagram of MOSMO Toolbox
List of files for MOSMO Toolbox
Setting up objective function from block parameter to
R
workspace in Matlab Simulink
R
Simulink
model properties setup
MOSMO Toolbox user interface
Define problem using GUI
Define problem using file function
Example of design space
Run the MOSMO Toolbox
Status of design space
Termination of MOSMO algorithm
Sampling option
Surrogate model option
Plot and Display
Training plot
Testing plot
Pareto-front and best compromise plot
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63
65
67
68
69
70
71
71
72
73
73
74
74
75
76
76
77
77
77
xiv
LIST OF ABBREVIATIONS
DoE
–
Design of Experiment
FFNN
–
Feed Forward Neural Network
GUI
–
Graphic User Interface
GUIDE
–
Graphic User Interface Development Environment
ISE
–
Integral Square Error
LH
–
Latin Hypercube
LHD
–
Latin Hypercube Design
LHS
–
Latin Hypercube Sampling
MIMO
–
Multi Input Multi Outputs
MOSMO
–
multi-objective optimization using surrogate modeling
NSGA-II
–
Non-dominated Sorting Genetic Algorithm II
PD
–
Proportional Derivative
PID
–
Proportional Integral Derivative
RBFNN
–
Radial Basis Function Neural Network
ROV
–
Remote Underwater Vehicle
SPEA2
–
Strength Pareto Evolutionary Algorithm 2
xv
LIST OF SYMBOLS
D
–
Input parameter
DE
–
Euclidean distance
Ex
–
Error
Ēx
–
Estimated error
lx
–
Lower bound
ux
–
Upper bound
k·k
–
Euclidean Norm
φk
–
Basis function
x ∈ <R×1
–
Input vector
φ
–
Pseudo
W ij
–
Weight for network from neuron i to j
wlk
–
Weight in the output layer
Kp
–
PID proportional gain
Ki
–
PID integral gain
Kd
–
PID derivative gain
xvi
LIST OF APPENDICES
APPENDIX
A
B
TITLE
LIST OF PUBLICATIONS
MAIN CODING
PAGE
89
90