# 臺灣博碩士論文加值系統

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 T-S 模糊模式(T-S fuzzy model)，是近幾年來廣泛使用於處理非線性系統的控制方法之一。此種模糊模式可將非線性系統以模糊理論中的IF-THEN規則庫來取代且其推論部為線性方程式的型式為他的主要特色，而且此模式化方式可近似或完整的表示原非線性系統。在設計控制器上使用所謂平行分布補償(PDC)的概念並利用線性系統的方法，最後將穩定性分析的問題轉換為線性矩陣不等式(LMIs)的型式並用Matlab 去求解。在討論T-S 模糊模式時，我們所探討的大都僅僅限於區域穩定，在討論區域穩定時，首先提出線性矩陣不等式(LMIs) 有解並不代表整個模式區域皆穩定，隨後提出如何找出這個穩定的區域，最後並提出全域穩定的條件。在追蹤控制上，我們藉由一些技巧可將原本複雜的追蹤系統轉換成比較簡單的型式。在此追蹤控制上，我們知道要量測所有的狀態在實際的系統中是不太可能，所以在此我們使用估測器來量測這些不可量測的狀態。在現實的情況中，系統的內部部分狀態如果不可得知，可能會造成控制器或估測器的前件部的變數狀態跟著無法得知，而這變數狀態必須取決於估測器，這是我們最後探討的課題。
 Recently, there has been a rapid growing interesting in using T-S fuzzy model to approximate nonlinear system. The T-S fuzzy model which originates from Takagi and Sugeno mainly deals with the nonlinear systems. With using this model, the nonlinear system is represented by several fuzzy subsystems in fuzzy IF-THEN rules where the con-sequent part is linear dynamical equation. Blending these IF-THEN rules, we can exactly represent the original nonlinear system. When consider the controller and observer design, we use the conception of parallel distributed compensation (PDC) to carry out these designs. We discuss the stability analysis of T-S fuzzy systems by using the Lyapunov's direct method. The sufficient conditions are formulated into linear matrix inequalities (LMIs). Typically, the stability analysis is investigated in local region due to the local sector nonlinearity. We introduce the concept of region of model and region of stability to characterize the stability property. The stability region can be obtained by using the level set of Lyapunov function. In addition, a global stability condition is addressed. As a second part of thesis, we discuss the tracking control of nonlinear systems by using T-S fuzzy model. To cope with the problem of immeasurable states, the observer-based fuzzy controller is our main concern. An H 1 performance criterion is proposed to attenuate the disturbance due to immeasurable premise variables. Furthermore, an asymptotical tracking can be achieved when the disturbance is with a Lischitz-type property. All the stability conditions and the derivation of control gains are converted into LMIs problems which can be solving by Matlab’s toolbox.
 Contents1IntroductoryChapter11.1Background...................................11.2 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Organization of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Takagi-Sugeno Fuzzy System 52.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Construction of T-S Fuzzy Model . . . . . . . . . . . . . . . . . . . . . . . 62.2.1 Sector Nonlinearity . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2.2 T-S Fuzzy Model [27] . . . . . . . . . . . . . . . . . . . . . . . . . . 102.3 Parallel Distributed Compensation . . . . . . . . . . . . . . . . . . . . . . 132.4 Linear Matrix Inequalities . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.5 Calculation of Summation Index . . . . . . . . . . . . . . . . . . . . . . . . 162.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 T-S Fuzzy Model with Local Sector Nonlinearity 183.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.2 T-S Fuzzy Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.3 Stability Region of T-S Fuzzy Model . . . . . . . . . . . . . . . . . . . . . 233.3.1 Determined by Vector Field on the Boundary . . . . . . . . . . . . 233.3.2 Determined By the Level Set of Lyapunov Function . . . . . . . . . 263.3.3 Extended to State-Feedback Systems . . . . . . . . . . . . . . . . . 263.4 Application on a Boost Converter . . . . . . . . . . . . . . . . . . . . . . . 283.5 Global Stability Analysis of T-S Fuzzy Model . . . . . . . . . . . . . . . . 363.6 Example for Global Stability . . . . . . . . . . . . . . . . . . . . . . . . . 383.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404 Tracking Control Design with Fuzzy Observer 434.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434.2 Basic Fuzzy Tracking Control . . . . . . . . . . . . . . . . . . . . . . . . . 444.2.1 Tracking Control Design . . . . . . . . . . . . . . . . . . . . . . . . 444.2.2 Stability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454.3 Measurable Premise Variables . . . . . . . . . . . . . . . . . . . . . . . . . 464.3.1 Fuzzy Observer Design . . . . . . . . . . . . . . . . . . . . . . . . . 464.3.2 Stability Conditions for Tracking Control . . . . . . . . . . . . . . . 484.4 Immeasurable Premise Variables . . . . . . . . . . . . . . . . . . . . . . . . 494.4.1 Tracking Control Based on Robust Performance . . . . . . . . . . . 514.4.2 Disturbance Satisfying Lipschitz Condition . . . . . . . . . . . . . . 544.5 Global Tracking Control Design with Observer . . . . . . . . . . . . . . . . 554.5.1 All Measurable States . . . . . . . . . . . . . . . . . . . . . . . . . 554.5.2 Measurable Premise Variables . . . . . . . . . . . . . . . . . . . . . 564.6 Simulation Results for Chua’s Circuit System . . . . . . . . . . . . . . . . 574.6.1 Simulation Result for Section 4.2 . . . . . . . . . . . . . . . . . . . 594.6.2 Simulation Result for Section 4.3 . . . . . . . . . . . . . . . . . . . 594.6.3 Simulation Result for Section 4.4 . . . . . . . . . . . . . . . . . . . 604.7 Example for Global Tracking Control . . . . . . . . . . . . . . . . . . . . . 604.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615 Conclusions 71References 72List of Figures2.1 Global sector nonlinearity . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2 For global sector nonlinearity, Á(x) is bounded by d1 and d2 . . . . . . . . 82.3 Local sector nonlinearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.4 For local sector nonlinearity, Á(x) is bounded by d1 and d2 only in theregion [ ¡ d; d] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.5 The concept of parallel distributed compensation . . . . . . . . . . . . . . 133.1 The vector field on boundary x1 = 2 . . . . . . . . . . . . . . . . . . . . . 233.2 The vector field on boundary x2 = 1 . . . . . . . . . . . . . . . . . . . . . 243.3 The state trajectory starting from initial state (0,5.3) . . . . . . . . . . . . 253.4 The stable region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.5 The Boost Converter Circuit . . . . . . . . . . . . . . . . . . . . . . . . . . 293.6 The state ˜ x response for the boost converter . . . . . . . . . . . . . . . . . 333.7 The state ˜ x response only for T-S fuzzy controller . . . . . . . . . . . . . . 343.8 The state ˜ x response for boost converter with initial state(-150,-50) . . . . 353.9 The state trajectory, membership function and controller with D = 1, d = 0:1 413.10 The state trajectory and controller with D = 1, d = 0:1 and D = 50, d = ¡ 50 424.1 Chua’s circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 574.2 Tracking control of x1(t) and x2(t) with measurable states . . . . . . . . . 624.3 Tracking control of x3(t) and controller input with measurable states . . . 634.4 Tracking and observer for x1(t) and x2(t) with known premise variables . . 644.5 Tracking and observer for x3(t) and controller input with known premisevariables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654.6 Tracking and estimation error for x(t) with known premise variables . . . . 664.7 Tracking and observer for x1(t) and x2(t) with unknown premise variables . 674.8 Tracking and observer for x3(t) and controller input with unknown premisevariables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 684.9 Tracking and estimation error for x(t) with unknown premise variables . . 694.10 Global Tracking control with measurable states . . . . . . . . . . . . . . . 70
 References[1] A. Isidori, and W. Kang, “H 1 control via measurment feedback for general nonlinearsystems,” IEEE Trans. Autom. Syst., vol. 40, pp. 446-472, Jan./Feb. 1995.[2] A. Jadbabaie, Robust, Non-Fragile Controller Sysnthesis Using Model-Based FuzzySystems: ALinear Matrix Inequality Approach, Master Thesis, Univ. New Mexico,pp. 28-41, 1997.[3] B. S. Chen, C. S. Tseng, and H. J. Uang, “Robustness design of nonlinear dynamicsystems via fuzzy linear control,” IEEE Trans. Fuzzy Syst., vol. 7, pp. 571-585, 1999.[4] B. S. Chen, C. S. Tseng, and H. J. Uang, “Mixed H2/H 1 fuzzy output feedbackcontrol design for nonlinear dynamic systems: an LMI approach,” IEEE Trans. FuzzySyst., vol. 8, pp. 249-265, 2000.[5] C. S. Tseng, and B. S. Chen, “Fuzzy tracking control design for nonlinear descrete-timedynamic system via T-S fuzzy model,” Fuzzy IEEE, vol. 1, pp. 405-410, 2000.[6] C. S. Tseng, B. S. Chen, and H. J. Uang, “Fuzzy tracking control design for nonlineardynamic systems via T-S fuzzy model,” Proc. Automatic Control Conf., Taiwan, pp.581-586, 2000.[7] C. S. Tseng, and B. S. Chen, “Fuzzy tracking control design for nonlinear dynamicsystems via T-S fuzzy model,” IEEE Trans. on Fuzzy Syst., vol. 9, NO. 3, pp. 381-392,June, 2001.[8] E. Kim, and H. Lee, “New Approaches to Relaxed Quadratic Stability Condition ofFuzzy Control Systems,” IEEE Trans. Fuzzy Syst., Man., vol. 8, NO.5, pp. 523-533,October, 2000.[9] I. Masubuchi, Y. Kamitane, A. Ohara, and N. Suda, “H 1 control for descriptorsystems: a matrix inequality approach,” Automatica, vol. 33, pp. 669-673, 1995.[10] J. Joh, Y.-H Chen, and R. Langari, “On the stability issues of linear Takagi-Sugenofuzzy models,” IEEE Trans. Fuzzy Syst., vol. 6, pp. 402-410, 1998.[11] L. A. Zadeh, “Fuzzy sets,” Inform. Contr., vol. 8, pp. 338-353, Jan./Feb. 1965.[12] T. Takagi, and M. Sugeno, “Fuzzy identification of systems and its applicationsto modeling and control,” IEEE Trans. Syst., Man, Cybern., vol. 15, pp. 116-132,Jan./Feb. 1985.[13] K. Tanaka, and M. Sugeno, “Stability analysis and design of fuzzy control systems,”Fuzzy Sets and Systems vol. 45, pp. 136-156, Feb. 1992.[14] K. Tanaka, and M. Sano, “A robust stabilization problem of fuzzy control systemsand it’s application to backing up control of truck-trailer,” IEEE Trans. Fuzzy Syst.,vol. 2, pp. 119-134, 1994.[15] H. O. Wang, K. Tanaka, and M. F. Griffin, “Parallel distributed compensation ofnonlinear systems by Takagi-Sugeno fuzzy model,” International Joint Conferenceof the Fourth IEEE International Conference on Fuzzy Systems and The Second In-ternationalFuzzy Engineering Symposium., Proceedings of 1995 IEEE InternationalConference on , vol. 2, pp. 20-24, March, 1995.[16] K. Tanaka, and M. Sano, “Trajectory stabilization of a model car via fuzzy control,”IEEE Trans. Fuzzy Syst., vol. 70, pp. 155-170, 1995.[17] H. O. Wang, K. Tanaka, and M. F. Griffin, “An approach to fuzzy control of nonlinearsystems: Stability and design issues,” IEEE Trans. Fuzzy Syst., vol. 4, pp. 14-23, Feb.1996.[18] K. Tanaka, T. Ikeda, and H. O. Wang, “Robust stabilization of a class of uncertainnonlinear systems via fuzzy control: quadratic stabilizability, H 1 control theory, andlinear matrix inequalities,” IEEE Trans. Fuzzy Syst., vol. 4, pp. 1-13, 1996.[19] K. Tanaka, T. Taniguchi and H. O. Wang, “Model-based fuzzy control for two trailersproblem : stability analysis and design via linear matrix inequalities,” Fuzzy IEEE,vol. 1, pp. 343-348, 1997.[20] K. Tanaka, T. Ikeda, and H. O. Wang, “An LMI approach to fuzzy controller designsbased on relaxed stability conditions,” Proceedings of the Sixth IEEE InternationalConference on Fuzzy Systems, vol. 1, pp. 171-176, 1997.[21] K. Tanaka, T. Ikeda, and H. O. Wang, “A unified approach to controlling chaos viaLMI-based fuzzy control system design,” IEEE Trans. Circ. Syst., vol. 45, no. 10,pp. 1021-1040, 1998.[22] K. Tanaka, T. Ikeda, and H. O. Wang, “Fuzzy regulators and fuzzy observer: Relaxedstability conditions and LMI-based designs,” IEEE Trans. Fuzzy Syst., vol. 6, pp.250-265, May, 2000.[23] K. Tanaka, T. Kosaki, and H. O. Wang, “Backing control problem of a mobile robotwith multiple trailers: fuzzy modeling and LMI-based design,” IEEE Trans. System,Man, Cybern, vol. 233, pp. 329-337, 1998.[24] T. Taniguchi, K. Tanaka, and H. O. Wang, “Fuzzy descriptor system and nonlinearmodel following control,” IEEE Trans. Fuzzy Syst., vol. 8, pp. 442-452, 2000.[25] K.-Y. Lian, T.-S. Chiang, C.-S. Chiu, and P. Liu, “Secure communications of chaoticsystems with robust performance via fuzzy observer-based design,” to appear in IEEETrans. Fuzzzy Syst., vol. 9, Feb. 2001.[26] K.-Y. Lian, C.-S. Chiu, and P. Liu, “LMI-based Fuzzy Chaotic Synchronization andCommunications,” to appear inSpecial Issue IEEE Trans. Fuzzy syst., 2001.[27] K.-Y. Lian, T.-S. Chiang, C.-S. Chiu, and P. Liu, “Synthesis of Fuzzy Model-baseddesigns to Synchronization and Secure Communications for Chaotic Systems,” IEEETrans. Syst., Man., Cybern., B, vol. 31, pp. 66-83, 2001.[28] S. Boyd, L. El Ghaoui, E. Feron, and V. Balakrishnan, Linear Matrix Inequalities inSystem and Control Theory. Philadelphia, PA [1994]: SIAM.[29] S. Kawamoto et al., “An Approach to Stability Analysis of Second Order FuzzySystems,” Proceedings of First IEEE International Conference on Fuzzy Systems,vol. 1, pp. 1427-1434, 1992.[30] Z. X. Han, G. Feng, B. L. Walcott, and J. Ma, “Dynamic Output Feedback ControllerDesign for Fuzzy System,”IEEE Trans. System, Man, Cybern vol. 30, pp. 204-210,2000.
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 1 小腦模型網路PID之質子交換膜燃料電池最大功率追蹤控制研究 2 無電流感測電源轉換器之模糊控制 3 具有H-infinity強健性之模糊模式追蹤控制 4 粒子群聚最佳化應用於倒車控制系統 5 船舶抗翻覆之模糊滑動模式控制與控制參數之最佳化分析 6 離散時間延遲系統之穩定性分析與迴授之穩定化設計 7 複雜動態網路系統之同步化分析與設計 8 無感測器質子交換膜燃料電池之電壓輸出追蹤模糊控制器設計 9 受性能條件限制之正規強健濾波器設計 10 非線性系統之LMI模糊模型預測控制－以類神經網路調整系統之等級函數 11 考慮二次成本函數之不定延遲奇異系統強健T-S模糊控制器設計 12 以LMI控制設計方法應用在離散時間非線性系統 13 考慮二次成本函數之不定延遲系統強健T-S模糊控制器設計 14 透過LMIs為磁浮系統設計T-S模糊控制器 15 模糊模式之狀態估測與穩定性分析

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 1 間接式卡門濾波器之訊息融合方法 2 以T-S模糊方法為基礎之無感測器感應馬達的向量控制 3 電源轉換器之積分型模糊控制器設計 4 升降壓型電源轉換器之T-S模糊模式控制 5 基於新型機器學習方法和電化學之偵測環境污染物的高效能傳感器 6 以田口法研究植物生長控制因子 7 考慮語速特徵的中文關鍵詞語音辨識 8 三維多聲源定位系統 9 基於卷積神經網路之雙模型技術應用於語音辨識系統 10 基於連通域標示法之分群演算法 11 基於模板韻律特徵之英語口說語調轉換系統 12 基於聲源定位之自走車跟隨控制 13 基於引擎聲及鳴笛聲進行車輛種類辨識 14 以英文關鍵詞進行語者性別與口音辨識的混合CNN-SVM模型 15 利用穿戴式裝置之生理信號進行情緒辨識與放鬆機制設計

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