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研究生： 廖建豪
Jian-Hao Liao
論文名稱： 簡化退火演算法基於模糊類神經網路控制器於非線性系統之控制
Reduced SA Fuzzy-neural Controller for Nonlinear Systems
指導教授： 呂藝光
Leu, Yih-Guang
王偉彥
Wang, Wei-Yen
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
語文別： 英文
論文頁數： 86
中文關鍵詞： 模擬退火演算法 、模糊類神經 、適應控制 、非線性控制
英文關鍵詞： simulated annealing algorithm, fuzzy neural networks, adaptive control, nonlinear systems
論文種類： 學術論文
相關次數： 點閱：193  下載：5 
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    • 本文提出一個利用簡化的模擬退火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。此簡化的模擬退火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模擬退火演算法來即時調整，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Lyapunov函數的推導過程中，提出一個能量成本函數於簡化的模擬退火最佳演算法中，藉著獲得較佳的閉迴路系統的穩定度。此外，由於簡化模擬退火法，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，加入監督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 reduced simulated annealing 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 reduced simulated annealing algorithm does not require the procedure of off-line learning and the complicated mathematical form. 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 reduced simulated annealing algorithm in order to generate the appropriate control input. For the purpose of on-line evaluating the stability of the closed-loop systems, an energy cost function derived from Lyapunov function is involved in the reduced simulated annealing algorithm. In addition, the system states may go into the unsafe region if the reduced simulated annealing algorithm can not instantaneously generate the appropriate weights. In order to guarantee the stability of the closed-loop nonlinear system, a supervisory 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.

    Contents ABSTRACT I 摘 要 III 誌　 謝 IV List of Figures VII List of Tables X Chapter 1 Introductions 1 1.1 Background 1 1.2 Motivation and Major Works 3 1.3 Thesis Overview 4 Chapter 2 Evolutionary Learning of Fuzzy-Neural Networks Using a Reduced Simulated Annealing Optimization Algorithm 7 2.1 Fuzzy-Neural Networks 7 2.2 Reduced Simulated Annealing Optimization (RSAO) Algorithm for Off-line Learning 9 2.3 Simulation 11 2.4 Conclusions 22 Chapter 3 Indirect RSA On-Line Tuning of Fuzzy-Neural Networks for Uncertain Nonlinear Systems 23 3.1 Problem Formulation 23 3.1.1 The Design of Certainty Equivalent Controller 23 3.1.2 Supervisory Control 26 3.2 Description of Reduce Simulated Annealing Algorithm for On-line Controllers 27 3.3 Simulation Examples of the RIAFC 29 3.4 Conclusions 35 Chapter 4 Backstepping Adaptive Control of Uncertain Nonlinear Systems Using RSA On-Line Tuning of Fuzzy-Neural Networks 37 4.1 Problem Formulation 37 4.1.1 The Design of Backstepping Controller 37 4.1.2 On-line Learning of Fuzzy-Neural Backstepping Control Using RSAOA 40 4.2 Simulation Examples of the RBAFC 43 4.3 Conclusions 49 Chapter 5 Design of Fuzzy-neural Controller Using Reduce Simulated Annealing Algorithms for MIMO Nonlinear Systems 50 5.1 Problem Formulation and Fuzzy-Neural Networks 50 5.1.1 Problem Formulation and Backstepping Control Design 50 5.1.2 Description of MIMO Fuzzy-neural Networks 53 5.2 Development of Simulated Annealing Adaptive Fuzzy-neural Control Scheme 54 5.3 Simulation Examples 57 5.4 Conclusions 62 Chapter 6 Design of Fuzzy-neural Controllers for DC Servo motors Using Reduced Simulated Annealing Algorithms 64 6.1 Problem Description of DC Servo Motors 64 6.2 Simulation Results 66 6.3 The Hardware Structure and Experimental Results 70 6.4 Conclusions 77 Chapter 7 Summaries and Suggestions for Future Research 78 7.1 Summaries 78 7.2 Suggestions for Future Research 79 References 80

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