臺灣博碩士論文加值系統

本論文之主旨係在於發展適應性強健型小腦模型控制器，其中利用具有動態特性的遞迴式小腦模型控制器為近似基礎並結合適應性控制、滑動模式控制與強健控制等理論，再依據李雅普諾夫穩定性定理設計遞迴式小腦模型控制器的參數適應性調整法則，因此整個閉迴路控制系統的穩定性可以被保證。最後並廣泛的應用在一些具有非線性且不確定系統之閉迴路控制上。本論文將探討所提出來的適應性強健型小腦模型控制器及其應用性。首先，將介紹小腦模型控制器並提出遞迴式小腦模型控制器設計。接著將針對單輸入-單輸出受控系統並根據上述所提出的遞迴式小腦模型設計控制器。最後將其應用於船舶航向控制、車輛追蹤防撞控制、線型超音波馬達位置控制、混沌電路等系統當中，同時並討論所設計之控制系統的優越性。在多輸入-多輸出控制系統方面，本論文針對系統分別提出不需數學模型及需要數學模型之控制法則。這些控制法則分別採用遞迴式小腦模型控制器為主控制器與當成不確定量的估測器。所開發出來的多輸入-多輸出適應性強健型小腦模型控制器並應用於解決一些具有高度非線性且時變系統的軌跡追蹤問題。其受控系統包括混沌電路系統、質量-彈簧-阻尼系統。最後本論文也利用遞迴式小腦模型控制器設計出失效容忍的兩足機器人強健控制法則。經由模擬與實作的結果顯示，對於這些具有不確定量且非線性之系統，本論文所提出的控制系統均能達到令人滿意的控制性能。

The purpose of this dissertation is to develop the adaptive robust cerebellar model articulation controller (CMAC) based on the dynamic characteristics of recurrent CMAC (RCMAC), and to integrate it with adaptive control, sliding mode control and robust control technologies for the control application to uncertain nonlinear systems. According to Lyapunov synthesis approach, the adaptive tuning laws of RCMAC can be derived and the system stability can be guaranteed. This dissertation introduces the structures of CMAC and RCMAC first. Then, the adaptive RCMACs are developed for the single-input single-output (SISO) nonlinear control systems; and they are applied to a ship heading control, a car-following control, a linear ultrasonic motor (LUSM) position control and a chaotic circuit control. Moreover, in multi-input multi-output (MIMO) control system design; this dissertation also proposes the adaptive robust control systems for the uncertain nonlinear MIMO systems. In this designs, RCMAC can be used as the main controller or the uncertainty estimator. The developed MIMO RCMAC adaptive robust control systems are then applied to a nonlinear chaotic circuit and a mass-spring-damper system. Furthermore, an RCMAC fault tolerant robust control of a biped robot is also presented. From the simulation and experimental results, the control schemes proposed in this dissertation have been shown to achieve satisfactory control performance for the considered nonlinear systems.

Contents
摘要 i
Abstract ii
Contents iii
List of Tables vi
List of Figures vii
Nomenclature x
Chapter 1 Introduction
1.1 General Remark and Overview of Previous Work 1
1.2 Objectives and Organization of the Dissertation 3
Chapter 2 Introduction of Cerebellar Model Articulation
Controller (CMAC) and Recurrent CMACs (RCMACs)
2.1 Overview 5
2.2 The Cerebellar Cortex 6
2.3 Original CMAC 7
2.4 General CMAC and RCMACs 11
2.4.1 The structures of CMAC and RCMAC 11
2.4.2. The structure of output-recurrent CMAC (ORCMAC) 14
Chapter 3 Intelligent Adaptive Control of SISO Nonlinear
Systems Using ORCMAC
3.1 Overview 24
3.2 Problem Formulation 27
3.3 Intelligent Adaptive Control System Design Using ORCMAC 28
3.3.1 Implementation of ORCMAC and compensation controller 29
3.3.2 On-line parameter training algorithm 31
3.3.3 Design procedure of IAC 34
3.4 Illustrative Examples 34
3.5 Summary 38
Chapter 4 Intelligent Adaptive Sliding Mode Control of SISO
Nonlinear Systems Using RCMAC as Model Identifier
4.1 Overview 47
4.2 Problem Formulation 48
4.3 Sliding-Mode Control System Design 50
4.4 Intelligent Adaptive Sliding-Mode Control System Design 51
4.5 Illustrative Examples 58
4.6 Summary 61
Chapter 5 Robust Adaptive Control of MIMO Uncertain Nonlinear
Systems Using RCMAC
5.1 Overview 68
5.2 Problem Statement 70
5.3 Robust Adaptive Controller Design 71
5.4 Illustrative Examples for RAC 77
5.5 Robust Adaptive Sliding Mode Controller Design 81
5.5.1 Implementation of RCMAC 82
5.5.2 On-line parameter learning scheme for RCMAC 82
5.5.3 supervisor design 83
5.6 Illustrative Examples for RASMC 85
5.7 Summary 88
Chapter 6 Robust Hybrid Sliding Model Control of MIMO
Uncertain Nonlinear Systems Using ORCMAC as
Uncertainty Estimator
6.1 Overview 97
6.2 Problem Formulation 99
6.3 Sliding Mode Control System Design 100
6.4 Robust Hybrid Sliding Mode Control System Design 101
6.4.1 RCMAC modeling uncertainty estimator 102
6.4.2 Robust hybrid sliding mode control with ORCMAC modeling 102
uncertainty estimator
6.4.3 On-line parameter learning 106
6.5 Simulation Results 107
6.6 Summary 110
Chapter 7 Robust Fault-Tolerant Control of Biped Robot
Using RCMAC as Fault Estimator
7.1 Overview 117
7.2 Biped Robot Dynamics and Controller Module 118
7.2.1 Biped robot dynamics 118
7.2.2 Controller module 119
7.3 Fault Estimation Module and Stability Analysis 121
7.4 Simulation Results 126
7.5 Summary 129
Chapter 8 Conclusions and Suggestions for Future Research
8.1 Conclusions 141
8.2 Suggestions for Future Research 142
Appendix
Appendix A: Ship heading control system 144
Appendix B: Car-following control system 144
Appendix C: Chaotic circuit 145
Appendix D: Mass-spring-damper mechanical system 146
Appendix E: Chua’s chaotic circuit 146
Reference 151
Biographical Sketch and Publication List
Biographical Sketch 156
Publication List 157


List of Tables
Table 7.1 The parameters of biped robot. 131
Table 7.2 The nominal parameters of biped robot. 131



List of Figures
Fig. 2.1 A model of the cerebellar cortex. 18
Fig. 2.2 The basic concept of an original CMAC. 18
Fig. 2.3 Structure of a 2-D CMAC. 19
Fig. 2.4 The organization of the receptive-fields for the 2-D CMAC in Fig. 2.3. 20
Fig. 2.5 Architecture of a general CMAC ( ) and an RCMAC ( ). 21
Fig. 2.6 Structure of a 2-D CMAC with and . 21
Fig. 2.7 Gaussian receptive-field basis function for receptive-field 22
Fig. 2.8 Architecture of an ORCMAC. 22
Fig. 2.9 A 2-D ORCMAC with and . 23
Fig. 3.1 Block diagram of ORCMAC-based intelligent adaptive control system. 40
Fig. 3.2(a) Numerical simulations for ship heading angle. 40
Fig. 3.2(b) Numerical simulations for ship heading angular velocity. 41
Fig. 3.2(c) The associated control effort (rudder angle). 41
Fig. 3.2(d) The tracking error of ship heading control. 42
Fig. 3.3 The change of frictional force. 42
Fig. 3.4(a). The velocity profiles of the lead vehicle and the following vehicle. 43
(0 denotes the lead vehicle).
Fig. 3.4(b). The tracking error of the following vehicle. 43
Fig. 3.4(c). The control effort of the following vehicle. 44
Fig. 3.4(d). The learned connection weights. 44
Fig. 3.4(e). The learned recurrent weights. 45
Fig. 3.5 PC-based LUSM experimental control system. 45


Fig. 3.6 Experimental results of ORCMAC-based intelligent adaptive control for LUSM: (a) The tracking response for nominal case. (b) The control effort for nominal case. (c) The tracking error for nominal case. (d) The tracking response for parameter variation case. (e) The control effort for parameter variation case. (f) The tracking error for parameter variation case. 46
Fig. 4.1 The block diagram of the SMC feedback control system. 62
Fig. 4.2 The block diagram of the IASMC feedback control system. 62
Fig. 4.3 Phase plane and state trajectories of the transformed chaotic circuit under
no control. 63 63
Fig. 4.4 State responses and control effort for the SMC chaotic circuit system. 64
Fig. 4.5 State responses and control effort for the IASMC chaotic circuit system. 65
Fig. 4.6 External noise of the car following system. 66
Fig. 4.7 Simulations of the car following system by using SMC. 66
Fig. 4.8 Simulations of the car following system by using IASMC. 67
Fig. 5.1 The block diagram of RAC system. 89
Fig. 5.2 Trajectory responses of RAC for mass-spring-damper system with 90
Fig. 5.3 Trajectory responses of RAC for mass-spring-damper system with 91
Fig. 5.4 Trajectory responses of RAC for Chua’s chaotic circuit with 92
Fig. 5.5 Trajectory responses of RAC for Chua’s chaotic circuit with 93
Fig. 5.6 The block diagram of RASMC feedback control system. 94
Fig. 5.7 The simulation results of Chua’s chaotic circuit using RASMC for Case 1. 95
Fig. 5.8 The simulation results of Chua’s chaotic circuit using RASMC for Case 2. 96
Fig. 6.1 The block diagram of SMC feedback control system. 111
Fig. 6.2 The block diagram of the RHSMC feedback control system. 111
Fig. 6.3 The chaotic circuit responses under no control. 112
Fig. 6.4 The simulation results of SMC for Case 1. 113
Fig. 6.5 The simulation results of SMC for Case 2. 114
Fig. 6.6 The simulation results of RHSMC for Case 1. 115
Fig. 6.7 The simulation results of RHSMC for Case2. 116
Fig. 7.1 A nine-link biped robot. 132
Fig. 7.2 Architecture of an RCMAC-based fault-tolerant control scheme. 132
Fig. 7.3(a) The joint angle of each link by equipping with CMAC for Case 1. 133
Fig. 7.3(b) The time history of the fault function and the output of CMAC for Case1. 133
Fig. 7.3(c) The associated control efforts by equipping with CMAC for Case 1. 134
Fig. 7.4(a) The joint angle of each link by equipping with CMAC for Case 2. 134
Fig. 7.4(b) The time history of the fault function and the output of CMAC for Case 2. 135
Fig. 7.4(c) The associated control efforts by equipping with CMAC for Case 2. 135
Fig. 7.5(a) The joint angle of each link by equipping with RCMAC for Case 1. 136
Fig. 7.5(b) The time history of the fault function and the output of RCMAC for Case 1. 136
Fig. 7.5(c) The associated control efforts by equipping with RCMAC for Case 1. 137
Fig. 7.6(a) The joint angle of each link by equipping with RCMAC for Case 2. 137
Fig. 7.6(b) The time history of the fault function and the output of RCMAC for Case 2. 138
Fig. 7.6(c) The associated control efforts by equipping with RCMAC for Case 2. 138
Fig. 7.7(a) The joint angle of each link by equipping with RCMAC for Case 2
(smaller learning-rates). 139
Fig. 7.7(b) The time history of the fault function and the output of RCMAC for Case 2 (smaller learning-rates). 139
Fig. 7.7(c) The associated control efforts by equipping RCMAC for
Case 2 (smaller learning-rates). 140

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