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研究生：	林建佑 Jian-You Lin
論文名稱：	遺傳演算模糊類神經與其在直流伺服馬達上之應用 Genetic Algorithms Fuzzy-Neural Controller and Its Application in DC Servo Motors
指導教授：	呂藝光 Leu, Yih-Guang
學位類別：	碩士 Master
系所名稱：	工業教育學系 Department of Industrial Education
論文出版年：	2009
畢業學年度：	97
論文頁數：	90
中文關鍵詞：	遺傳演算法、模糊類神經、適應控制、非線性控制
英文關鍵詞：	genetic algorithm, fuzzy neural networks, adaptive control, nonlinear systems
論文種類：	學術論文
相關次數：	點閱：209 下載：0
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本文提出一個使用小型的遺傳演算法來調整模糊類神經網路的參數，並將其應用於函數近似與非線性系統之適應控制器設計。此小型的遺傳演算法應用於適應控制器設計，不需要事先離線學習的程序和複雜的數學運算。相較於傳統非線性系統的適應控制器，可有效減少適應控制器所需複雜的數學運算。在非線性系統之適應控制過程中，模糊類神經控制器的權重値是經由遺傳演算法來即時調整，以產生適當的控制輸入。為了即時評估閉迴路系統穩定的趨勢，本文從里亞布諾夫（Lyapunov）函數的穩定性分析推導過程中，提出一個能量適應函數於小型的遺傳最佳演算法中，藉此獲得較佳的閉迴路系統的穩定度。此外，由於小型的遺傳演算法可能在即時控制過程中使系統狀態進入不安全的區域。因此，加入安全控制器以限制閉迴路系統的狀態進入不安全的區域。
　　本文藉由電腦模擬結果驗證所提出方法的可行性與效能。最後，將此模糊類神經控制器應用在直流伺服馬達追蹤控制實驗。

In this thesis, a compact genetic algorithm used to tune the parameters of fuzzy neural networks is proposed for function approximation and adaptive control of nonlinear systems. For the design of adaptive controller, the compact genetic algorithm does not require the procedure of off-line learning and the complicated mathematical computation. Compared with traditional adaptive controllers, computation loading can be effectively alleviated. In adaptive control procedure for nonlinear systems, the weights of the fuzzy neural controller are online adjusted by the compact genetic algorithm in order to generate appropriate control input. For the purpose of on-line evaluating the stability of the closed-loop systems, an energy fitness function derived from Lyapunov function is involved in the compact genetic algorithm. In addition, the system states may go into the unsafe region if the compact genetic algorithm can not instantaneously generate the appropriate weights. In order to guarantee the stability of the closed-loop nonlinear system, a safe controller is incorporated into the fuzzy neural controller.
　　Finally, some computer simulation examples and a servo motor experiment are provided to demonstrate the feasibility and effectiveness of the proposed method.

ABSTRACT.................................................I
摘    要................................................III
誌　　謝.................................................IV
CONTENTS.................................................V
LIST OF FIGURES.......................................VIII
LIST OF TABLES.........................................XII
Chapter 1 Introduction...................................1
1 Background...........................................1
2 Motivation and Purpose...............................3
3 Organization of the Thesis...........................4
Chapter 2 The Learning of Fuzzy-neural Networks Using Compact Genetic Algorithms...............................6
2 Fuzzy-Neural Networks................................6
2.1 Fuzzy Inference Method.............................7
3 Overview of Genetic Algorithms.......................9
4 Evolutionary Processes of the Compact Genetic Algorithms (CGA)........................................10
4.1 Population Initialization.........................11
4.2 Energy Fitness function...........................12
4.3 Single Gene Crossover Operation...................12
4.4 Sorting Operation.................................14
4.5 Compact Mutation Operation........................15
5 Conclusions.........................................16
Chapter 3 CGA-based On-Line Tuning of Fuzzy-neural Networks for Uncertain Nonlinear Systems.........................17
1 Overview............................................17
2 Backstepping Control Systems........................18
2.1 Problem Formulation...............................18
2.2 The Design of Backstepping Controller.............18
3 Development of Genetic Adaptive Backsteping Fuzzy-neural Control Scheme...................................21
3.1 Designing the Genetic Adaptive Backstepping Fuzzy-neural   Controller.....................................22
3.2 Safe Controller...................................23
4 Simulation Examples.................................25
5 Conclusions.........................................33
Chapter 4 Design of Fuzzy-neural Controller Using Compact Genetic Algorithms for MIMO Nonlinear Systems...........34
1 Overview............................................34
2 Problem Formulation and MIMO Fuzzy-Neural Networks..35
2.1 Problem Formulation and Backstepping Control Design
      ..................................................35
2.2 Description of MIMO Fuzzy-neural Networks.........39
3 Description of MIMO Compact Genetic Algorithm.......40
4 Development of Genetic Adaptive Fuzzy-neural Control Scheme..................................................42
5 Simulation Results..................................45
6 Conclusions ........................................51
Chapter 5 Design of Fuzzy-neural Controller Using Compact Genetic Algorithms for Robot Manipulators...............52
1 Overview............................................52
2 Formulation of System Model    ........................53
3 Backstepping Control Design    ........................54
4 Development of Genetic Adaptive Fuzzy-neural Control Scheme for Manipulators.................................56
5 Simulation Results..................................59
6 Conclusions.........................................65
Chapter 6 Design of Fuzzy-neural Controllers for DC Servo motors Using Compact Genetic Algorithm..................66
1 Overview............................................66
2 Problem Description of DC Servo motor...............66
3 Computer Simulation Results.........................68
4 Introduction of Experiment..........................73
4.1 Hardware Framework................................74
4.2 Experimental Results..............................76
5 Conclusions.........................................81
Chapter 7 Summaries and Suggestions for Future Research.83
1 Summaries...........................................83
2 Suggestions for Future Research.....................84
References..............................................85
                                

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