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研究生:許振榕
研究生(外文):Chen-Gjung Hsu
論文名稱:基於增益餘量與相位餘量之非穩定系統PID控制器的調整方法:利用模糊類神經網路
論文名稱(外文):Tuning of PID Controllers for Unstable Processes Based on Gain and Phase Margin Specifications: A Fuzzy Neural
指導教授:鄧清政
指導教授(外文):Ching-Cheng Teng
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電機與控制工程系
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:1999
畢業學年度:87
語文別:英文
論文頁數:73
中文關鍵詞:模糊類神經網路增益餘量相位餘量倒傳遞學習法則PID控制器
外文關鍵詞:FNNFNGPFuzzyNeuralGain marginPhase marginPID controller
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在本論文中,我們提出一個根據增益邊際與相位邊際的規格,用模糊類神網路經來決定PID控制器的參數。過去PID控制器已經很廣泛地應用在穩定系統中,但對於開迴路的非穩定系統的PID控制器卻較少提到。在本文中,我們針對開迴路非穩定系統,先用模糊類神經網路去訓練增益邊際與相位邊際的規格與PID控制器參數間的關係之後,再利用已訓練過的網路去得到一組符合使用者所需求的增益邊際與相位邊際的規格之PID控制器參數,而不需靠任何的數值分析或作圖法來決定。此網路即使給的規格可能不合理,它依舊會給我們一組合理卻又離規格比較近的PID控制器參數。從模擬中可知模糊類神經網路可以有效率地達到所要求的規格。

In the thesis, we present a PID tuning method for unstable processes using Fuzzy Neural Network based on gain and phase margin (FNGP) specifications. PID tuning methods were widely used to control stable processes. However, PID control for unstable processes is less common. A fuzzy neural network approach is proposed to identify the relationship between the gain-phase margin specifications and the PID controller parameters. Then, the FNN is used to automatically tune the PID controller parameters for different gain and phase margin specifications so that neither numerical methods nor graphical methods need be used. Even though for some of the unreasonable specifications, the FNN still can find a suitable PID controllers' parameters close to the specifications. Simulation results show that the FNN can achieve the specified values efficiently.

Abstract (Chinese)i
Abstract (English)ii
Acknowledgments (Chinese)iii
Contentsiv
List of Figuresvi
List of Tablesix
Chapter 1 Introduction1
Chapter 2 Gain and Phase for unstable processes 5
2.1 Gain and Phase Margins5
2.2 Gain and Phase Margin Method (GPM) 9
Chapter 3 Fuzzy Neural Network15
3.1 Fuzzy Inference System and Neural Network15
3.2 Structure of Fuzzy Neural Network17
3.3 Layers operation of the FNN19
3.4 Supervised Learning22
Chapter 4 Tuning of PID Controllers Based on Gain and Phase Margins Using FNGP26
4.1 Structure of the Controller Using Fuzzy Neural Network based on Gain and Phase Margins (FNGP)26
4.2 Create training data29
4.3 The procedure of FNGP tuning method 33
Chapter 5 Simulation Results36
5.1 Valid region for first-order unstable processes36
5.2 Comparison with GPM method for first-order stable and unstable process39
5.3 Some higer-order simulation47
5.3.1 Second-order with time-delay process47
5.3.2 Third-order with time-delay 49
5.4 PID controller simulation 52
Chapter 6 Conclusion57
Bibliography59
Appendix62

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