具自我學習能力之智慧型控制系統設計

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姓名

邱健榮(Chien-Jung Chiu) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

具自我學習能力之智慧型控制系統設計
(Design of an intelligent control system with online learning algorithms)

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摘要(中)

一般控制器設計需要事先知道受控系統方程式，但該方程式往往很難獲得，本論文提出了多種智慧型控制系統的設計方法，將適應性控制、類神經網路控制與滑動模式控制等的技巧結合應用在控制系統參數學習能力上。首先在適應性比例積分微分控制器設計方面，透過最陡坡降法調整法則達到線上調整控制器增益參數。在適應性類神經網路控制器設計方面，則提出回饋型小波類神經網路適應性控制、輻狀基底類神經網路適應性控制與模糊小波類神經網路適應性控制系統，分別利用了最陡坡降法調整法則與李亞普諾夫穩定定理論推導出系統參數的學習法則，因此可保證經由線上調整系統參數後均能使得系統趨向於穩定，並提出PID型式學習法則加快追蹤誤差與系統參數收斂速度。進一步，本文提出一動態滑動模式類神經網路適應性控制器用來解決控制訊號抖動缺點。最後，本文將所開發之多種智慧型控制方法分別運用於感應伺服馬達定位控制、無刷直流馬達定位控制、混沌控制系統以及混沌系統同步控制，經由模擬或實驗結果均可發現所提出方法之可行性。

摘要(英)

The traditional control system designs are always based on the system dynamic equations; however, it is difficult to be described as the plants are too complex. This dissertation proposes several intelligent control systems based on the adaptive control, sliding-mode control and neural network control technologies. For the adaptive proportional integral derivative (PID) controller design, the adaptive PID controller can automatically tune the controller gain factors based on the gradient descent method. For the adaptive neural network controllers design, a recurrent-wavelet-neural-network-based adaptive control, RBF-neural-network-based adaptive control and fuzzy-wavelet-neural-network-based adaptive control methods are proposed. In these control system designs, an online parameter tuning methodology, using the gradient descent method or the Lyapunov stability theorem, is developed to increase the learning capability and to guarantee the system’s stability. Moreover, a PID type adaptation tuning mechanism is derived to speed up the convergence of the tracking error and controller parameters. Furthermore, the dynamic-sliding-mode-neural-network-based adaptive control design method is developed with dynamic learning rate which is proposed to reduce the chattering phenomenon. Finally, the developed control system design methods are applied to some control system applications, such as induction servomotor system, brushless DC motor system, chaotic system and chaotic synchronization system. The simulation and experimental results have demonstrated that the effectiveness of the proposed design methods.

關鍵字(中)

★ 模糊小波類神經網路
★ 回饋型小波類神經網路
★ 輻狀基底類神經網路

關鍵字(英)

★ fuzzy wavelet neural network
★ recurrent wavelet neural network
★ radial basis function neural network

論文目次

中文摘要 i
Abstract ii
誌謝 iii
Content iv
List of Figures vii
List of Tables ix
Chapter 1 Introduction 1
1.1 General Remark and Overview of Previous Work 1
1.2 Objectives and Organization of the Dissertation 2
Chapter 2 APIDC with Fuzzy Compensator 4
2.1 Overview 4
2.2 Design of APIDC System 5
2.2.1 PID Controller Design 6
2.2.2 Fuzzy Compensator Design 8
2.2.3 Stability Analysis of APIDC System 9
2.3 Experimental Results 12
2.3.1 Problem Statement 12
2.3.2 Experimental Results of APIDC System 13
2.4 Summary 15
Chapter 3 PI-ADSNC with Fuzzy Compensator 19
3.1 Overview 19
3.2 Problem Statement 19
3.2.1 SMC System Design 20
3.2.2 DSMC System Design 22
3.3 Design of PI-ADSNC System 23
3.4 Experimental Results 29
3.5 Summary 30
Chapter 4 PID-AFWNNC with Saturation Compensator 35
4.1 Overview 35
4.2 Description Fuzzy Wavelet Neural Network 36
4.3 Design of PID-AFWNNC System 38
4.4 Problem Statement 43
4.5 Simulation and Experimental Results 44
4.6 Summary 46
Chapter 5 ADRBFNC with Dynamical Learning Rate 54
5.1 Overview 54
5.2 Dynamic Sliding-Mode Controller Design 55
5.3 Design of ADRBFNC system 58
5.3.1 Online Learning Algorithm 59
5.3.2 Dynamical Learning Rate Parameters 60
5.3.3 Switching Compensator Design 66
5.4 Simulation Results 68
5.5 Summary 70
Chapter 6 Conclusions and Suggestions for Future Research 81
6.1 Conclusions 81
6.2 Suggestions for Future Research 82
References 84
Biographical Sketch and Publication List 92

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指導教授

許駿飛、蔡章仁
(Chun-Fei Hsu、Jang-Zern Tsai)

審核日期

2012-7-24

推文