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研究生:邱政宏
研究生(外文):Chung-Hung Chiu
論文名稱:隨機模糊T-S模式的適應最小變異控制
論文名稱(外文):Adaptive Minimum Variance Control of Stochastic Fuzzy T-S Modes
指導教授:李柏坤
指導教授(外文):Bore. Kuen Lee
學位類別:碩士
校院名稱:中華大學
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:2007
畢業學年度:95
語文別:英文
論文頁數:56
中文關鍵詞:隨機模糊系統最小變異控制
外文關鍵詞:stochasticfyzzy systemminimun variance control
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在這一次研究中是引用隨機T-S模糊ARMAX模式的適應最小變異控制技巧。在模糊ARMAX模式中,模糊的一步估測模式是最先成熟的。隨機梯度演算法被提議用來判別關於一步估測的參數。針對適應控制方法,最小變異控制是用來找一個控制法則使得輸出追蹤到參考訊號。適應隨機模糊控制系統的穩定度和效能分析有嚴格推導。模擬討論也證實這些結果。
Adaptive minimum variance control for stochastic T-S fuzzy ARMAX model is addressed in this study. From the fuzzy ARMAX model, a fuzzy one-step ahead prediction model is first developed. A stochastic gradient algorithm is then proposed to identify the parameters of the related one-step-ahead predictor. Under the direct adaptive control scheme, minimum variance control is applied to find the control law to make the output track a desired reference signal. Stability and performance of the adaptive stochastic fuzzy control system are rigorously derived. Simulation study is also made to verify the developed results.
CONTENTS1.Introduction 4 1.1 Survey of Related Literature............................4 1.2 Motivation and Objective................................5 1.3 Organization of Thesis..................................6 1.4 Notations and Definitions...............................6 2.System modeling and problem formulation 8 3.Adaptive Minimum Varience Control of T-S Fuzzy Model 12 3.1 Stability of stochastic T-S fuzzy systems...............12 3.2 Optimal predictor of stochastic fuzzy systems...........17 3.3 Stochastic gradient algorithm...........................19 3.4 Adaptive Minimum variance control.......................23 3.5 Analysis of stability and tracking performance..........25 3.6 Simulation Study........................................27 4.Conclusions 30 A.Appendix 31 A.1 Proof of Theorem 1......................................31 A.2 Proof of Theorem 2......................................34 A.3 Proof of Lemma 1........................................35 A.4 Proof of Lemma 2........................................38 A.5 Proof of Theorem 3......................................39 A.6 Proof of Lemma 3........................................42 A.7 Proof of Lemma 4........................................44 A.8 Proof of Lemma 5........................................48 LIST of FIGURES 3.1 Membership functioms of Example 1.........................28 3.2 Output y(t) and its prediction y(t) of Example 1..........29 3.3 The reference signal y*(t) and the output y(t) of Example 1 is shownin the upper trace. The tracking error is shown in the lower trace.........................29
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