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研究生:陳德育
研究生(外文):Te-YU Chen
論文名稱:智慧型自組織控制系統設計
論文名稱(外文):Intelligent Self-Organizing Control System Design
指導教授:林志民林志民引用關係
學位類別:博士
校院名稱:元智大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:113
中文關鍵詞:模糊類神經網路小腦模型控制器自組織技術
外文關鍵詞:fuzzy neural networkcerebellar model articulation controllerself-organizing technology
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本論文基於適應性控制及模糊滑動控制設計一種新型智慧型自組織控制系統,目的在解決複雜非線性的控制系統問題及控制器架構的最佳化設計,所提出的控制器包含了主動控制器及補償控制器,主動控制器目的在於近似系統的最佳控制器模型,採用了模糊類神經網路控制器或小腦模型控制器,輔助控制器則是藉由 追蹤特性來減少剩餘的近似誤差以提昇系統控制性能。自組織是一個運用在智慧型控制器架構設計上的嶄新技術,控制器的架構設計不需要依靠過去的設計經驗即可根據控制狀態進行自動調整。在本論文中,以模糊類神經網路中的模糊規則數,及小腦模型控制器中相關記憶體空間中的層數與每一層中基本元素組成的區塊來進行架構上的調整。
在智慧型方面,為了讓系統有最佳化的參數,則採用最陡坡降法及李亞普諾夫穩定理論來增加網路收斂速度,並確保系統之穩定性;最後應用以系統模擬及實務操作來驗證所提出之智慧型自組織控制器之可行性,包含以下模組:Duffing振盪器、彈簧-阻尼器系統、橋式混沌電路、雙車倒單擺、線性超音波馬達及無刷直流馬達,成功的模擬及實作成果驗證設計之智慧型自組織控制系統是具有良好的控制性能。
This dissertation focused on a novel design of intelligent self-organizing control system based on adaptive control and fuzzy sliding-mode control for the uncertain nonlinear systems. The proposed control scheme is comprised of a main controller and an auxiliary compensation controller. The main controller, a fuzzy neural network (FNN) controller or a cerebellar model articulation controller (CMAC), is utilized to approximate an ideal controller and an auxiliary compensation controller is utilized to attenuate the residual approximation error with a specified tracking performance. The self-organizing technology is a modern skill for adjusting the structure of control system by itself without the need for preliminary knowledge and it can reduce the calculation loading for the control system. In this dissertation, the fuzzy rules in FNN and the numbers of layer and block in CMAC will be adjusted automatically.
In these intelligent self-organizing control systems design, the on-line parameter tuning methodology using both of the gradient descent method and the Lyapunov stability theorem is developed to increase the system learning capability and to guarantee the stability of the system. The developed control system design methods are then applied to some control system applications, such as chaotic Duffing system, mass-spring-damper system, Chau’s chaotic circuit system, inverted double pendulums system, linear ultrasonic motor (LUSM) system and brushless DC (BLDC) motor for demonstrating the effectiveness of the proposed design methods.
Contents
書名頁 i
論文口試委員審定書 ii
授權書 iii
摘要 iv
Abstract v
誌謝 vi
Contents vii
List of Figures ix
Nomenclature xi
Chapter 1 Introduction
1.1 General Remark and Overview of Previous Work 1
1.2 Objectives and Organization of the Dissertation 5
Chapter 2 Intelligent Tracking Control for Chaotic Nonlinear Systems Using Self-Organizing Fuzzy Neural Network
2.1 Overview 7
2.2 Problem Statement and Ideal Controller 8
2.3 Problem Formulation 11
2.4 Design of Intelligent Tracking Control 12
2.5 Simulation Results 16
2.6 Summary 17
Chapter 3 Intelligent Adaptive Control for MIMO Uncertain Nonlinear Systems Using RCMAC
3.1 Overview 23
3.2 RCMAC Network Architecture 23
3.3 Problem Statement 26
3.4 Intelligent Adaptive Controller Design 28
3.5 Illustrative Examples 33
3.6 Summary 39
Chapter 4 Self-Organizing CMAC Control for a Class of MIMO Uncertain Nonlinear Systems
4.1 Overview 47
4.2 Problem Formulation 48
4.3 Adaptive SOCM Control System Design 49
4.4 Simulation and Experimental Results 57
4.5 Summary 63
Chapter 5 Adaptive Full-Tune Self-Organizing CMAC Control for Nonlinear Systems
5.1 Overview 73
5.2 Adaptive FSOCM Control System Design 74
5.3 FSOCM-Based Adaptive Controller Design 82
5.4 Experimental Results 87
5.5 Summary 89
Chapter 6 Conclusions and Suggestions for Future Research
6.1 Conclusions 99
6.2 Suggestions for Future Research 100
Reference 102
Biographical Sketch and Publication List
Biographical Sketch 111
Publication List 112
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