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研究生:張偉德
研究生(外文):Wei-Der Chang
論文名稱:適應性控制器設計之一些論點
論文名稱(外文):Some Aspects of Adaptive Controller Design
指導教授:謝哲光謝哲光引用關係
指導教授(外文):Jer-Guang Hsieh
學位類別:博士
校院名稱:國立中山大學
系所名稱:電機工程學系研究所
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:2002
畢業學年度:90
語文別:英文
論文頁數:76
中文關鍵詞:PID 控制器遺傳演算法適應性控制自調諧神經元
外文關鍵詞:auto-tuning neuronPID controllergenetic algorithmadaptive control
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摘要
本論文將對一類非線性系統提出數種適應性控制設計的方法。首先,研究的主題是有關於自調諧PID控制器的設計。設計PID控制器主要的關鍵在於如何決定其三個控制增益值:即比例增益 、積分增益 、微分增益 。基於Lyapunov方法,吾人嘗試利用適應性控制的技巧,針對某類部分未知的非線性系統設計PID控制器,如此三個PID控制增益是可被線上做調整,進而獲得較佳的輸出響應,同時藉由引入監督式控制及具有投影式的修正型調整律,使得整個閉迴路的PID控制系統的穩定性可以確保。第二部份,基於使用簡單的自調諧類神經元,吾人將考慮兩種類型的適應性類神經控制系統,包括:直接式與間接式的適應性控制。首先將對自調諧類神經元做些說明,同時利用這種類神經元來取代原有在直接式與間接式的適應性類神經控制系統上的傳統類神經網路,如此可以大幅簡化所需的計算時間與網路的複雜性,而且也易於硬體的實現。第三個部份,基於模仿自然界的演化法則,遺傳演算法可在某個搜尋的範圍內,對一最佳化的問題求得最佳或者接近最佳的解。藉由最小化積分絕對值誤差的性能指標,吾人使用一種實數型編碼的遺傳演算法則來設計PID控制器,文中將指出吾人的結果與其它方法比較時的一些改進。同時,在每個部份的最後,將提供一些電腦模擬的結果來顯示吾人所提出方法的可行性。
ABSTRACT
In this dissertation, several adaptive control design schemes for a class of nonlinear systems are proposed. The first topic of the research is concerned with self-tuning PID controller design. The main problem of designing PID controller is how to determine the values of three control gains, i.e., proportional gain , integral gain , and derivative gain . We attempt to use the technique of adaptive control based on the Lyapunov approach to design the PID controller for some class of partially known nonlinear systems. Three PID control gains are adjusted on-line such that better output performance can be achieved. The stability of the closed-loop PID control systems is analyzed and guaranteed by introducing a supervisory control and a modified adaptation law with projection. Second, two kinds of adaptive neural control systems including the direct and indirect neural controls are considered by using simple single auto-tuning neuron. We will first propose a novel neuron called auto-tuning neuron and use it to take place of the roles of the traditional neural networks used in the direct and indirect adaptive neural control systems. This can greatly reduce the computational time and network complexities due to the simple configuration of the auto-tuning neuron. It is also easy for hardware implementation. Third, based on the idea borrowed from natural evolution, genetic algorithm can search for optimal or near-optimal solutions for an optimization problem over the search domain. An optimization technique of real-coded genetic algorithm is used to design the PID controller by minimizing the performance index of integrated absolute error. The improvements of our results over that using other methods are also illustrated. In the last part of each section, some computer simulation results will also be provided to illustrate our proposed methods.
CONTENTS
誌謝i
摘要ii
ABSTRACTiii
LIST OF FIGURESiv
CHAPTER 1 INTRODUCTION1
1.1 Motivation ……………………………………………………………...1
1.2 Brief Sketch of the Contents …………………………………………...4
CHAPTER 2 ADAPTIVE PID CONTROL6
2.1 Introduction …………………………………………………………….6
2.2 PID Controller ………………………………………………………….7
2.3 Self-tuning PID Control System ……………………………………….7
2.4 Illustrative Example …………………………………………………..15
CHAPTER 3 ADAPTIVE NEURAL CONTROL22
3.1 Introduction …………………………………………………………...22
3.2 Auto-tuning Neuron …………………………………………………..23
3.3 Direct Adaptive Neural Control ………………………………………25
3.4 Indirect Adaptive Neural Control …………………………………….39
CHAPTER 4PID CONTROLLER DESIGN BASED ON REAL-CODED GENETIC ALGORITHM52
4.1 Introduction …………………………………………………………...52
4.2 Genetic Algorithm and Real-coded Genetic Algorithm ……………...53
4.3 Real-Coded Genetic algorithm-based PID Controller Tuning ……….54
4.4 Illustrative Examples …………………………………………………58
CHAPTER 5 DISCUSSIONS AND CONCLUSIONS65
REFERENCES68
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