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研究生:張雅羚
研究生(外文):Ya-Ling Chang
論文名稱:非線性離散時間延遲系統之適應預估控制
論文名稱(外文):Adaptive Predictive Control of a Class of Nonlinear Discrete-Time Systems with Time Delay
指導教授:蔡清池
口試委員:李祖聖黃國勝林惠勇林志民蘇順豐莊家峰
口試日期:2016-07-20
學位類別:博士
校院名稱:國立中興大學
系所名稱:電機工程學系所
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:2016
畢業學年度:104
語文別:英文
論文頁數:104
中文關鍵詞:適應控制模糊模型化模糊類神經網路模糊小波類神經網路(FWNN)廣義預估控制(GPC)反模型控制模型參考適應控制非線性離散時間延遲系統預估控制遞迴小波類神經網路(RWNN)自調PID控制
外文關鍵詞:Adaptive controlfuzzy modelingfuzzy neural networkfuzzy wavelet neural networks (FWNN)generalized predictive control (GPC)inverse modeling controlmodel reference adaptive controlnonlinear discrete-time time-delay systemspredictive controlrecurrent wavelet neural networks (RWNN)self-tuning PID control
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本論文針對某類型非線性離散時間延遲系統,探討並提出四種適應預估控制方法,用以不僅可保證系統穩定,且可達成精確追蹤及干擾拒絕的性能。為建置完整適應預估控制系統,本文的內容可區分為四大核心研究課題。第一是結合模糊模型化方法與穩定廣義預估控制策略而提出一具模糊模型之適應穩定廣義預估控制(FASGPC)。第二是整合一個TSK型式的遞迴模糊類神經網路(TRFNN)適應反模型前饋控制器以及一個隨機適應模型參考預估控制器(SAMRPC)來組成一個新穎的智慧型適應雙自由度控制。第三是發展一個使用遞迴小波類神經網路(RWNN)之適應預估PID(RWNN-APPID)控制策略;該控制器是由RWNN鑑別器、調整機制及適應預估PID控制器所建製而成。第四是運用模糊小波類神經網路(FWNN),並運用適應預估控制來調整PID控制器參數,以達成滿意的控制性能。最後藉由幾類非線性離散時間延遲系統,進行數個電腦模擬與性能比較研究,用以驗證所提方法之有效性與優越性。

This dissertation presents several adaptive predictive control methods of a class of nonlinear discrete-time time-delay systems not only for guaranteed stability but also for precise setpoint tracking and disturbance rejection. To synthesize these overall adaptive predictive control systems, the research topics of the dissertation are classified into four core techniques. First, an adaptive stable generalized predictive control with fuzzy modeling (FASGPC) is proposed for combining the fuzzy modeling method and stable generalized predictive control (SGPC) strategy. Second, an intelligent adaptive two-degrees-of-freedom control is presented for combining a Takagi-Sugeno-Kang (TSK) type recurrent fuzzy neural network (TRFNN) adaptive inverse model feedforward controller with a stochastic adaptive model reference predictive controller (SAMRPC). Third, an adaptive predictive proportional-integral-derivative (PID) control approach by utilizing recurrent wavelet neural networks (RWNN-APPID) is derived and examined by the recurrent wavelet neural networks (RWNN) identifier, an adjusting mechanism and an adaptive predictive PID controller. Fourth, an adaptive predictive PID control via fuzzy wavelet neural networks (FWNN-APPID) is established by utilizing the fuzzy wavelet neural networks (FWNN) to tune PID parameters such that the proposed adaptive predictive PID control (FWNN-APPID) is capable of providing satisfactory control performance for a class of highly nonlinear discrete-time systems with time-delay. The effectiveness and merits of all the proposed methods are well exemplified by conducting several simulations on many nonlinear discrete-time time-delay systems.

CONTENTS
摘 要 i
Abstract ii
CONTENTS iii
LIST of TABLES vi
LIST of FIGURES vii
NOMENCLATURE x
ACRONYM xvi

Chapter 1 Introduction 1
1.1. Research Background 1
1.2. Literature Review 3
1.2.1 Related Work on Model Predictive Control (MPC) 3
1.2.2 Related Work on Fuzzy Control and Neural-Networks Control 4
1.2.3 Related Work on Adaptive PID control 6
1.3. Motivation and Objectives 8
1.4. Main Contributions 8
1.5. Dissertation Organization 9
Chapter 2 Adaptive Stable Generalized Predictive Control Using TSK Fuzzy Model 11
2.1. Mathematical Model and Parameter Estimation in Fuzzy Modeling 11
2.1.1. Mathematical Model in TSK Fuzzy Modeling 12
2.1.2. Antecedent Parameter Estimation in Fuzzy Modeling 13
2.1.3. Consequent Parameter Estimation in Fuzzy Modeling 15
2.2. Adaptive Stable Generalized Predictive Control 17
2.2.1. Stable Feedback Loop Design 18
2.2.2. Output Prediction 19
2.2.3. Stable Generalized Predictive Control 20
2.2.4. Real-Time Adaptive Control Strategy 25
2.3. Simulations and Discussion 26
2.4. Concluding Remarks 30
Chapter 3 Stochastic Adaptive Model Reference Predictive Control with TRFNN Adaptive Inverse Model Feedforward Controller 32
3.1. Recurrent Fuzzy Neural Network Learning Algorithm 33
3.2. TRFNN Adaptive Inverse Modeling Feedforward Control 36
3.3. Stochastic Adaptive Model Reference Predictive Feedback Control 37
3.4. Integral Control Algorithm 41
3.5. Simulations and Discussion 43
3.6. Concluding Remarks 48
Chapter 4 Adaptive Predictive PID Control Using Recurrent Wavelet Neural Networks 50
4.1. RWNN Modeling 51
4.1.1. RWNN Incremental Modeling 51
4.1.2. Update laws for RWNN parameters 53
4.2. Controller Design 56
4.2.1. Adaptive Predictive PID Controller Design Using RWNN 56
4.2.2. Update Rules for RWNN-APPID gains 57
4.2.3. Stability Analysis 59
4.3. Real-Time Control Algorithm 61
4.4. Simulations and Discussion 63
4.5. Concluding Remarks 71
Chapter 5 Adaptive Predictive PID Control Using Fuzzy Wavelet Neural Networks 72
5.1. FWNN Modeling 73
5.1.1. FWNN Incremental Modeling 73
5.1.2 Update laws for FWNN parameters 75
5.2. Controller Design 78
5.2.1 Adaptive Predictive PID Controller Design Using FWNN 78
5.2.2 Update Rules for FWNN-APPID gains 79
5.2.3 Stability Analysis 81
5.2.4 Real-Time Control Algorithm 83
5.3. Simulations and Discussion 84
5.4. Concluding Remarks 93
Chapter 6 Conclusions and Recommendations 94
6.1 Conclusions 94
6.2 Recommendations 96
Bibliography 98

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