Mô tả:
공학박사 학위논문
2 축 공압 인공근육 매니퓰레이터의
추정 및 제어에 관한 비교 연구
Comparison of Identification and Control of
2-Axes PAM Manipulator
울산대학교 대학원
기계자동차 공학부
Ho Pham Huy Anh
2 축 공압 인공근육 매니퓰레이터의
추정 및 제어에 관한 비교 연구
Comparison of Identification and Control of
2-Axes PAM Manipulator
지도교수 안경관
이 논문을공학박사학위 논문으로 제출함
2008 년 11 월
울산대학교 대학원
기계자동차 공학부
Ho Pham Huy Anh
ii
Ho Pham Huy Anh 의 공학박사 학위 논문을 인준함
심사위원장 이병룡
(인)
심사위원 양순용
(인)
심사위원 하철근
(인)
심사위원 박중호
(인)
심사위원 안경관
(인)
울산대학교 대학원
기계자동차 공학부
2008 년 11 월
3
Thesis for the Degree of Doctor of Philosophy
Comparison of Identification and Control of
2-Axes PAM Manipulator
By
Ho Pham Huy Anh
Advisor: Prof. KYOUNG KWAN AHN
School of Mechanical and Automotive Engineering
Graduate School
University of ULSAN
November 2008
Comparison of Identification and Control of
2-Axes PAM Manipulator
By
Ho Pham Huy Anh
Advisor: Prof. KYOUNG KWAN AHN
Submitted to the School of Mechanical and Automotive Engineering
in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
At
Graduate School, University of ULSAN
November 2008
ii
Comparison of Identification and Control of
2-Axes PAM Manipulator
A Dissertation
By
Ho Pham Huy Anh
Approved of styles and contents by:
Chairman
BYUNG RYONG LEE
Advisor
KYOUNG KWAN AHN
Member
SOON YOUNG YANG
Member
CHEOL GEUN HA
Member
JUNG HO PARK
November 2008
iii
Acknowledgments
This thesis would not have been completed without the help and unlimited support from
professors, colleagues, friends, and my love-family from whom I receive the encouragement,
the opportunity, the confidence and by so to whom I want to dedicate my best grateful.
Firstly, I want to express my sincere gratitude to my advisor, Prof. Kyoung Kwan Ahn, for
all of his guidance, advice and support during the course of my research and thesis writing.
Forever I will remember the opportunities he has provided me, for his constant support and his
initiative ideas and suggestions. My respect for him will always be in my mind.
I am also honored to have Prof. Byung Ryong Lee, Prof. Soon Young Yang, Prof. Cheol
Geun Ha and Prof. Jung Ho Park in my committee, whose inspiration, support and
perseverance made this dissertation become possible. I would like to thank them for their
interest and encouragement throughout this research.
No words for me to express my sincere gratitude towards all my Korean, Bangaldesh and
Vietnamese friends (Thanh-Hon-Nam-Hao-Kha-Tu-Truong-Hanh-Hung-JongIl-Amin-Mafuz
and others). Not much happy people like me to have their best friend. Thanks for helping me
to pass through difficult moments, for yours deep thinking and yours contributions to the
realization of my thesis, and especially for the many animated discussions on the subject.
This thesis is dedicated to my darling wife Le Tan Loi, my sons Bim-Bum and my girl Bo.
Special sentiment is also expressed to my sisters, my brother Huy Don and their family for
taking care of me during the time I studied abroad.
Finally I dedicate this work to my father and my late mother. Their endless love for me
always supports me in all my life.
November 2008
Ho Pham Huy Anh
iv
Contents
Acknowledgments
iv
Contents
v
List of Figures
vii
List of Tables
xi
Nomenclatures
xii
Abstract
xiii
Part I: Introduction
1.
2.
1
Introduction
2
1.1
Overview
2
1.2
Motivation
4
1.3
Outline of Thesis
6
Configuration, experiment setup and characteristic of pneumatic artificial muscle (PAM) manipulator
10
2.1
Introduction
10
2.2
Configuration, experiment setup and characteristic of 2-axes PAM manipulator
11
2.2.1
Configuration of 2-axes PAM manipulator system
11
2.2.2
Experiment setup
12
2.2.3
Configuration of 1-axes PAM manipulator system
14
2.2.4
Basic characteristic of PAM manipulator
16
Part II: Intelligent Models and Model-Based Advanced Control Schemes of 2-Axes PAM Manipulator
3.
Modeling and Control of the 1-Axes PAM Manipulator using MGA-based NARX Fuzzy model
22
3.1
Introduction
22
3.2
Modified genetic algorithm (MGA) for NARX fuzzy model Identification
23
3.2.1
Conventional genetic algorithm (GA)
23
3.2.2
Modifications to genetic algorithm (MGA)
24
3.2.3
Modified genetic algorithm (MGA) for optimizing fuzzy model’s parameters
27
3.3
MGA-based PAM manipulator NARX fuzzy model identification
31
3.4
Configuration of PAM manipulator system and PRBS training data
33
3.5
Design and Implementation of MGA-based NARX fuzzy model
35
3.6
Results of MGA-based PAM manipulator NARX fuzzy model identification
40
3.6.1
GA-based PAM manipulator TS fuzzy model identification
40
3.6.2
MGA-based PAM manipulator TS fuzzy model identification
44
3.6.3
MGA-based PAM manipulator NARX fuzzy model identification
49
3.7
4.
20
Conclusion
60
Modeling and Model-based Control of 1-Axes PAM Manipulator using Neural NARX model.
v
62
4.1
Introduction
62
4.2
Modeling of 1-Axes PAM manipulator using neural NARX model and INCBP algorithm
63
4.2.1
Recurrent neural NARX model and Back-Propagation (BP) learning algorithm
63
4.2.2
INCBP learning algorithm of Neural NARX model identification
68
4.2.3
Modeling of PAM manipulator Neural NARX model
70
4.3
Experimental results
72
4.4
Advanced Control of PAM manipulator based on neural NARX model
88
4.4.1
PAM manipulator forward and inverse neural NARX model identification
89
4.4.2
Proposed Hybrid Neural NARX Internal Model (NARX-IMC-PID) Control
95
4.4.3
Experimental results
98
4.5
5.
108
Modeling and Control of 2-Axes PAM Manipulator using MGA-based Double NARX fuzzy model
109
5.1
Introduction
109
5.2
Modified genetic algorithm (MGA) for NARX fuzzy model Identification
110
5.3
Identification of 2-axes PAM manipulator based on Double NARX fuzzy model
111
5.4
Identification of Inverse and Forward Double NARX fuzzy model
115
5.5
Experimental results
120
5.5.1
Identification of 2-axes PAM manipulator Forward Double NARX fuzzy model
120
5.5.2
Identification of 2-axes PAM manipulator Inverse Double NARX fuzzy model
5.6
5.7
6.
Conclusion
124
5.6.1
Implementation of MGA-based inverse NARX fuzzy model
125
5.6.2
Results of MGA-based Inverse NARX Fuzzy model Identification
126
5.6.3
Hybrid Online DNN-PID Feed-forward Inverse NARX Fuzzy Control scheme
130
5.6.4
Experimental results
135
Conclusion
143
Modeling and Control of 2-Axes PAM Manipulator using Neural MIMO NARX model
144
6.1
Introduction
144
6.2
Proposed MIMO Neural NARX model and BP learning algorithm
145
6.3
Identification of Inverse and Forward Neural MIMO NARX model
147
6.4
Proposed Hybrid online neural MIMO NARX Feed-forward PID control system
155
6.4.1
Controller design
155
6.4.2
Experiment setup
158
6.4.3
Experimental results
158
6.5
Conclusion
170
Part IV: Conclusion and discussion
7.
122
Advanced Control of PAM manipulator based on Inverse NARX Fuzzy model
172
Conclusion and discussion
173
References
177
Publications
184
vi
List of Figures
Figure 2.1 Structure of the PAM
11
(a) Working of PAM
(b) PAM – FESTO Product
(c) The structure of PAM
Figure 2.2 General configuration of 2-axes PAM manipulator
12
Figure 2.3: Working principle of the 2-axes PAM manipulator
12
Figure 2.4a Schematic diagram of the 2-axes PAM manipulator
13
Figure 2.4b Experimental Configuration of the 2-axes PAM manipulator system
14
Figure 2.5 Block diagram for obtaining PRBS input-output data of the 1-link PAM manipulator
15
Figure 2.6 Block diagram of the experimental apparatus of the 1-link PAM manipulator
16
Figure 2.7 Basis Characteristics of the PAM
17
Figure 2.8 Hysteresis of the PAM
18
Figure 2.9 h -F relationships of artificial muscle (extracted from (FESTO, 2005)
[29]
)
18
Figure 3.1: The flow chart of conventional GA optimization procedure
25
Figure 3.2: The flow chart of Modified MGA optimization procedure.
30
Figure 3.3 Procedure of the PAM manipulator NARX Fuzzy Model Identification
30
Figure 3.4a Block diagram of The MGA-based PAM manipulator’s TS Fuzzy Model Identification
32
Figure 3.4b Block diagram of The MGA-based PAM manipulator’s NARX11 Fuzzy Model Identification
32
Figure 3.4c Block diagram of The MGA-based PAM manipulator’s NARX22 Fuzzy Model Identification
33
Figure 3.5 Experiment data obtained from the PAM manipulator
34
Figure 3.6a Training data obtained from the PAM manipulator
34
Figure 3.6b Validating data obtained from the PAM manipulator
34
Figure 3.7 Validating pseudo-PRBS data obtained from the PAM manipulator
35
Figure 3.8 Triangle input membership function with spacing factor = 2
36
Figure 3.9a The Seed Points and the Grid Points for Rule-Base Construction
37
Figure 3.9b Derived Rule Base
37
Figure 3.10 Fitness Convergence GA-based Fuzzy Model Identification of the PAM manipulator
40
Figure 3.11a Estimation of GA-based Fuzzy Model of the PAM manipulator
41
Figure 3.11b Validation of GA-based Fuzzy Model of the PAM manipulator
41
Figure 3.11c Membership Input-Output & Surf-Viewer of GA-based Fuzzy Model Identification
42
Figure 3.11d Convergence of Principal Parameters of GA-based Fuzzy Model Identification
43
Figure 3.12 Fitness Convergence MGA-based Fuzzy Model Identification of the PAM manipulator
45
Figure 3.13a Membership Input-Output & Surf-Viewer of MGA-based Fuzzy Model Identification
46
Figure 3.13b Estimation of MGA-based TS Fuzzy Model of the PAM manipulator
47
Figure 3.13c Validation of MGA-based TS Fuzzy Model of the PAM manipulator
47
Figure 3.13d Convergence of principal parameters of the MGA-based Fuzzy Model of the PAM manipulator
48
Figure 3.14 Fitness Convergence MGA-based NARX11 Fuzzy Model Identification of the PAM manipulator
50
Figure 3.15a Membership Input-Output & Surf-Viewer of MGA-based NARX11 Fuzzy Model Identification
51
Figure 3.15b Estimation of MGA-based NARX11 Fuzzy Model of the PAM manipulator
52
Figure 3.15c Validation of MGA-based NARX11Fuzzy Model of the PAM manipulator
52
vii
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