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 제어
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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
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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
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3
Ho Pham Huy Anh 의 공학박사 학위 논문을 인준함
울산대학교 대학원 기계자동차 공학부
2008 년 11 월
심사위원장 이병룡 (인)
Trang 4Thesis 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
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ii
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
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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
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iv
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
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Contents
Part I: Introduction 1
1 Introduction 2
1.1 Overview 2 1.2 Motivation 4 1.3 Outline of Thesis 6
2 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 20
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 Conclusion 60
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vi
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 Conclusion 108
5 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 122
5.6 Advanced Control of PAM manipulator based on Inverse NARX Fuzzy model 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
5.7 Conclusion 143
6 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 172
7 Conclusion and discussion 173
References 177
Publications 184
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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