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研究生： 董倫騰
TUNG, LUN TENG
論文名稱： 可調整擺錘之倒單擺追蹤系統的設計
The Design of Tracing Control System for Inverted Pendulum with Adjustable Clapper
指導教授： 曾煥雯
Tzeng, Huan-Wen
學位類別： 碩士
Master
系所名稱： 機電工程學系
Department of Mechatronic Engineering
論文出版年： 2007
畢業學年度： 95
語文別： 中文
論文頁數： 116
中文關鍵詞： 倒單擺 、可調整擺錘 、極點配置 、自適應網路模糊推論系統
英文關鍵詞： Adaptive Network-Based Fuzzy Inference System, ANFIS, pole placement, inverted pendulum, adjustable clapper
論文種類： 學術論文
相關次數： 點閱：227  下載：39 
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    • 本論文提出一個“可調整擺錘”之倒單擺追蹤系統的研究。所採用的控制法則，為極點配置法與自適應網路模糊推論系統(ANFIS) 設計方法，各別運用於系統的平衡定位控制與平衡追蹤控制。
    依據工程力學的原理，完成“可調整擺錘”之倒單擺的數學模型推導，並將其線性化以求出輸入量與輸出量的關係式。以此關係式利用極點配置設計方法，完成了平衡定位控制的模擬。此外也針對平衡追蹤控制，運用自適應網路模糊推論系統學習產生模糊控制器，提出可行的控制的方法。
    比較其結果，平衡定位控制為平衡追蹤控制的特例，但以自適應網路模糊推論系統學習所得到的模糊控制法則較極點配置法之性能為佳。以自適應網路模糊推論系統所學習而得的模糊控制器具有較佳的效率和強健性，不但可以在平衡追蹤控制有不錯的表現，亦可運用在平衡定位控制上。

    This research proposed a tracing controller system for an inverted pendulum with an adjustable clapper. The pole placement method and ANFIS(Adaptive Network-Based Fuzzy Inference System) are applied to position-balance control and trace-control individually.
    According to the principle of engineering mechanics, we’ve obtained a linearized input-output expression from the mathematics model of the controlled object. Afterwards this research could simulate balance-positioning control using pole-placement method from the derived expression. We also pin-point the balance-tracing control system using ANFIS method and produce a feasible controlling method.
    By comparing “balance-positioning” and “balance-tracing” methods, we conclude the ANFIS method is better than the pole-placement method. This research obtained the fuzzy controller from the learning of ANFIS, it has a superior performance and robustness, not only would it perform well in balance-tracing but also in balance-positioning as well.

    中文摘要 I ABSTRACT II 目錄 III 圖目錄 VII 表目錄 XIII 第一章 緒論 1 1-1 研究背景與動機 1 1-2 研究目的 6 1-3 研究範圍與限制 7 1-4 研究方法 7 1-5 研究步驟 8 第二章 文獻探討 11 2-1 文獻回顧 11 2-2 滑車直線運動之簡單無擺錘倒單擺系統的數學模型推導 12 2-2-1 數學模型之動力微分方程式的推導(牛頓—尤拉方法) 13 2-2-2 系統的轉移函數 15 2-2-3 系統的狀態空間方程式 16 2-2-4 數學模型的動力微分方程式的推導(能量法) 17 2-3 模糊理論 21 2-4 模糊集合 22 2-4-1 模糊集合之基本性質 23 2-4-2 模糊集合之基本運算 26 2-5 模糊推論 28 2-5-1 模糊推論方式 30 2-5-2 具有模糊化和模糊解化的模糊邏輯系統 32 2-5-3 Sugeno型模糊邏輯系統 32 2-6 模糊控制 34 2-7 SUGENO型模糊推論系統設計 38 2-8自適應網路模糊推論系統 42 3-9 非線性受控系統之數學模型的線性化 47 第三章 系統架構設計 51 3-1 系統機構的建構 51 3-2 開發控制系統的流程 53 3-3 可調整擺錘之倒單擺系統的數學模型 54 3-4可調整擺錘之倒單擺的平衡定位控制模擬(極點配置設計法) 71 3-5可調整擺錘之倒單擺的追蹤控制模擬(ANFIS設計法) 72 3-5-1 可調整擺錘之倒單擺系統的模型 73 3-5-2 被控制對象模型線性化 75 3-5-3 Sugeno型自適應類神經網路控制器設計 76 3-5-4 決定輸入變量空間 78 3-5-5 決定模糊輸入空間數據點的選取 78 3-5-6 模糊空間數據點輸出計算 79 3-5-7 訓練ANFIS生成模糊推論系統 79 3-5-8 模型追蹤模擬 80 第四章 模擬步驟與結果 81 4-1極點配置設計之平衡定位控制模擬 83 4-2 ANFIS設計之平衡追蹤控制模擬 94 4-2-1應用ANFIS的離線訓練控制架構 94 4-2-2弦波追蹤控制模擬結果 98 4-2-3方波追蹤控制模擬結果 101 4-2-4鋸齒波追蹤控制模擬結果 102 4-2-5隨機波形追蹤控制模擬結果 105 第五章 結論與後續研究建議 109 5-1結論 109 5-2後續研究建議 109 參考文獻 111 作者簡歷 115

    [1] C.C. Lee, "Fuzzy logic in control systems: Fuzzy logic control - Part I, II", IEEE Trans. Syst., Man, Cybern., vol. 20, pp. 404-435, Mar. 1990.
    [2] J. R. Layne, K. M. Passino, and S. Yurkovich, "Fuzzy learning control for antiskid braking systems", IEEE Trans. Contr. Syst. Technol., vol. 1, pp122-129, June 1993.
    [3] B. Kosko and S. Isaku, "Fuzzy Logic", Scientific American, July 1993.
    [4] J. Bezdek, "Fuzzy Models - what are they, and why?", IEEE Trans. On Fuzzy System, vol. 1, Feb 1993.
    [5] C.L. Huang and C.Y. Hsieh, "A Neuro-Adaptive Variable Structure Control for Partially Unknown Nonlinear Dynamic System and Its Application", IEEE Trans. Contr. Syst. Technol., vol. 10, no2, pp263-271, Mar. 2002.
    [6] T. Yamakawa, "A Fuzzy Inference Engine in Nonlinear Analog Mode and Its Application to a Fuzzy Logic Control", IEEE Transactions on Neural Networks, vol. 4, pp496-522,May 1993.
    [7] J. Bezdek, "Fuzzy Models - what are they, and why?", IEEE Trans. on Fuzzy Systems, vol. 1, Feb. 1993.
    [8] B. Kosko and S. Isaku, "Fuzzy Logic", Scientific American, July 1993.
    [9] Jyh-Shing Roger Jang, "ANFIS: Adaptive-Network-Based Fuzzy Inference System", IEEE Trans. On Fuzzy System, vol. 23, MAY/JUNE 1993.
    [10] B. Shahian and M Hassul, "Control System Design Using Matlab, ch. Ball-on-Beam Balancer", pp. 465-476. Englewood Cliffs, NJ: Prentice Hall, 1994.
    [11] Chin E. Lin and Yih-Ran Sheu, "A Hybrid-Control Approach for Pendulum-Car Control", IEEE Trans. On industrial electronics, vol.39, no.3, June 1992.
    [12] Moeljono Widjaja and Stephen Yurkovich, "Intelligent Control for Swing Up and Balancing of an Inverted Pendulum System", Proceedings of the 4th IEEE Conference on Control Applications, 1995, pp. 534-542.
    [13] Haihua Gao and Xingyu Wang, "Simulation Research on Extension Adaptive Control of Inverted Pendulum", IEEE Proceedings of the 5th World Congress on Intelligent Control and Automation, june 15-19, 2004.
    [14] Alan Bradshaw and Jindi Shao, "Swing-up control of inverted pendulum systems", Robotica, vol. 14, pp. 397-405, 1996, Cambridge University Press.
    [15] C. S. Chen and W. L. Chen, "Robust adaptive sliding-mode control using fuzzy modeling for an inverted-pendulum system", IEEE Trans. Ind. Electron., vol.45, pp. 297-306, 1998.
    [16] T. S. Li and M. Y. Shieh, "Switching-type fuzzy sliding mode control of a cart-pole system", Mechatronics, vol. 10, pp. 91-109, 2000.
    [17] Feijun Song and Samuel M. Smith, "Combination of Adaptive-Network Based Fuzzy Inference System and Incremental Best Estimate Directed Search", IEEE International Fuzzy Systems Conference, 2001.
    [18] Ali Ghanbari and Mohammad Farrokhi, "Decentralized Neuro-Fuzzy Controler Design Using Decoupled Sliding Mode Structure for Two Dimensional Inverted Pendulum", IEEE International Fuzzy Systems Conference, 2006.
    [19] C.C. Lee, "Fuzzy logic in control systems: Fuzzy logic control - Part I, II", IEEE Trans. Syst., Man, Cybern., vol. 20, pp. 404-435, Mar. 1990.
    [20] E. H. Mamdani, "Applications of Fuzzy Algorithms for Simple Dynamic Plant", Proc. IEEE, Vol.121, No.12, pp.1585-1588, 1974.
    [21] E. H. Mamdani and S.Assilian, "An Experiment in Linguistic Sythesis with a Fuzzy Logic Controller," Int. Journal of Man Machine Studies, Vol.7, No.1, pp.1-13, 1975.
    [22] W. Z. Qiao and M. Mizumoto, "PID Type Fuzzy Controllers and Parameters Adaptive Method", Fuzzy Sets and Systems, Vol.78, pp.23-35,1996.
    [23] F. L. Lewis and K. Liu, "Towards a Paradigm for Fuzzy Logic Control", Automatica, Vol.32, No.2, pp.167-181, 1996.
    [24] J. X. R.M.H. Cheng and S. LeQuoc, "Neuromorphic controller for AGV steering", in Proceedings of IEEE Int. Conf. on Robotics and Automation, (Nice, France), Vol. 3, pp.2057-2062, May 1992.
    [25] 孫宗瀛、楊英魁編著，“Fuzzy 控制：理論、實作與應用”，全華科技圖書股份有限公司，1997年出版。
    [26] C. C. Lee, "Fuzzy Logic in Control Systems : Fuzzy Logic Lontroller – Part I, Part II", IEEE Trans. on Systems, Man, and Cybernetics, Vol.20 No.2, pp.404-433, 1990.
    [27]. J. X. R.M.H. Cheng and S. LeQuoc, "Neuromorphic controller for AGV steering", in Proceedings of IEEE Int. Conf. on Robotics and Automation, (Nice, France), Vol. 3, pp.2057-2062, May 1992.
    [28] 王進德、蕭大全編著，“類神經網路與模糊控制理論入門”，全華技圖書股份有限公司，1994年出版。
    [29] 張麗秋、黃浩倫編箸，“類神經網路：理論與實務”，台灣東華出版社，2003年出版。
    [30] Jang J. S. R., Sun C. T. and Mizutani E., " Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Matlab Curriculum Series", 1997, Prentice-Hall International Inc.
    [31] Mendel J. M., "Uncertain Rule-Based Fuzzy Logic System: Introduction and New Directions", 2001, Prentice Hall PTR.
    [32] Victor Williams and Kiyotoshi Matsuoka, "Learning to Balance the Inverted Pendulum using Neural Network", Proceedings of IEEE International Joint Conference on Neural Networks, 1991, vol.1, pp.214-219.
    [33] John Nelson and L. Gordon Kraft, "Using CMAC Neural Networks and Optimal Control", Proceedings of IEEE International Conference on Neural Networks, 1995, vol.5, pp.2386-2390.

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