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研究生:洪堃能
研究生(外文):Kun-Neng Hung
論文名稱:適應性小波類神經網路控制運用於直流轉直流電源轉換器
論文名稱(外文):Adaptive Wavelet Neural Network Control for DC/DC Power Converter
指導教授:林志民林志民引用關係
指導教授(外文):Chih-Min Lin
學位類別:碩士
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:2006
畢業學年度:94
語文別:英文
論文頁數:70
中文關鍵詞:直流轉直流電源轉換器適應性小波類神經網路最佳學習法則強健性追蹤效能
外文關鍵詞:DC/DC power converteradaptive wavelet neural networkoptimal learning algorithmrobust tracking performance
相關次數:
  • 被引用被引用:1
  • 點閱點閱:241
  • 評分評分:
  • 下載下載:5
  • 收藏至我的研究室書目清單書目收藏:1
智慧型控制器非常適合應用於未知變數的非線性系統,與不易由傳統控制方法實現之控制器。
本文的目的是研究通用的直流轉直流電源轉換器之適應性小波類神經網路控制,所提的方法較傳統類神經網路更具有良好的穩定性與收斂性。首先針對直流轉直流電源轉換器與所提出之演算法逐一說明,最後從前饋式直流轉直流電源轉換器之實作上,列表如安定時間及動態超越量與傳統控制器做一比較,證明利用適應性小波類神經網路控制器來實現直流轉直流電源轉換器之可行性。
Intelligence controller is suitable for uncertain nonlinear systems, and to those not easy to be realized by classical design methods.
In this thesis, a wavelet neural network controller for DC/DC power converter is investigated. The presented approaches are better than traditional design methods. First the DC/DC power converter and the proposed algorithms are described. Finally, settling time and percentage overshoot for startup and step response are compared to traditional controls. From the experimental results of forward DC/DC power converter, it can be demonstrated the possibility of applying intelligence control method in practical DC/DC power converter design.
Chinese abstract i
Abstract ii
Acknowledgement iii
Contents iv
List of Figures vi
List of Tables viii
1. Introduction 1
1.1 General Remark 1
1.2 Overview of Previous Work 1
1.3 Organization and Contribution of the Thesis 4
2. Introduction of DC/DC Converter 5
2.1 Overview of DC/DC Converter 5
2.1.1 Conventional controller design 5
2.1.2 Intelligent controller design 6
2.2 Forward DC/DC Power Converter Modeling 7
2.3 Wavelet Neural Network 10
2.4 PC-based Realization 13
3. Adaptive Wavelet Neural Network Control with Optimal Learning Algorithm 21
3.1 Introduction 21
3.2 Design of Adaptive Wavelet Neural Network Control 22
3.3 Convergence Analyses 25
3.4 Experimental Results 29
3.4.1 AWNNC with constant learning-rates 29
3.4.2 AWNNC with Variable Optimal Learning-rates 30
3.5 Summary 31
4. Adaptive Wavelet Neural Network Control with Robust Tracking Performance 46
4.1 Introduction 46
4.2 Control System Design 46
4.3 Experimental Results 51
4.4 Summary 52
5. Conclusions and Suggestions for Future Research 61
5.1 Conclusions 61
5.2 Suggestions for Future Research 62
References 64
Autobiography 69
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