簡易檢索 / 詳目顯示

研究生: 翁瑞華
Ray-Hwa Wong
論文名稱: 耦合適應性自組織滑動模糊控制器在雙軸同步控制之研究
Study of Coupled Adaptive Self-Organizing Sliding-Mode Fuzzy Controller in Dual-Axis Synchronous Control
指導教授: 修芳仲
Fang-Jung Shiou
口試委員: 施明璋
Ming-Chang Shih
黃緒哲
Shiuh-Jer Huang
黃安橋
An-Chyau Huang
江茂雄
Mao-Hsiung Chiang
學位類別: 博士
Doctor
系所名稱: 工程學院 - 機械工程系
Department of Mechanical Engineering
論文出版年: 2013
畢業學年度: 101
語文別: 中文
論文頁數: 124
中文關鍵詞: 泵控系統閥控系統耦合適應性自組織滑動模糊控制器學習率適應性增益
外文關鍵詞: adaptive self-organizing controller, coupled controller, adaptation gain, sliding-mode fuzzy controller, valve-controlled system, pump-controlled system
相關次數: 點閱:545下載:11
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 本文中雙軸泵控和閥控系統是耦合系統,它們有明顯的結構耦合作用。泵控系統使用兩組動力單元來驅動兩個子系統,兩個子系統的同步差異較小。閥控系統由一組動力單元來驅動兩組液壓伺服閥驅動的子系統,兩個子系統的同步差異較大。
    本文提出一種耦合適應性自組織滑動模糊控制器,來改善它們的同步控制性能。耦合適應性自組織滑動模糊控制器由兩組基本的單軸滑動模糊控制器、模糊規則自我學習機構、適應性法則以及附調節器的耦合控制器組合而成。耦合適應性自組織滑動模糊控制器藉由各別的參數改變來學習不同的參數效應。實驗結果顯示模糊規則自我學習機構的學習率能減少同步誤差改善追蹤性能。此外,它也可改善閥控系統的穩態收斂性。適應性法則能改善追蹤誤差。然而,適應性法則對閥控系統追蹤誤差的改善較不明顯。附調節器的耦合控制器能改善兩個子系統的同步誤差。
    本研究以不同的負載用來模擬彎折過程中不同的工件及不同的耦合強度,實驗結果顯示,耦合適應性自組織滑動模糊控制器應用到泵控和閥控系統,有卓越的同步控制性能,而且對於不同的耦合強度具有強韌性。本文中並比較了泵控和閥控系統的暫態和穩態性能,實驗結果顯示,泵控系統有取代閥控系統的可行性。


    The double-axial pump-controlled and valve-controlled systems are coupled systems and have significant structural interaction. Pump-controlled system has two power units to drive two sub-systems, the synchronous difference is low. Valve-controlled system has one power unit to drive two sub-systems, the synchronous difference is a little bit higher.
    This dissertation proposes a coupled adaptive self-organizing sliding-mode fuzzy controller (CASOSMFC) to improve their level control performance. CASOSMFC is associated with two basic single-axial sliding-mode fuzzy controllers, self-learning fuzzy rule mechanisms, adaptive laws, coupled controllers and regulators. Then, it can base on variety parameters to study their parametric effects individually. Experimental results indicate that the learning rate of self-learning fuzzy rule mechanism can reduce synchronous error and improve the tracking performance. Besides, astringency in steady state of valve-controlled system has also been improved. Adaptive law improves tracking error. However, the improvement in valve-controlled system is insignificant. Coupled controllers and regulators improve synchronous error.
    Different loadings are used to simulate different work pieces and various coupled intensity folding processes. Experimental results indicate that CASOSMFC applied in the pump-controlled and valve-controlled system has excellent level control performance and has strong robustness to different coupled intensity.
    Via a variety of loading experiments, this dissertation provides comparisons of control performances between pump-controlled and valve-controlled system. Experimental results indicate that pump-controlled system is an option to replace valve-controlled system in synchronous control application.

    目錄 摘要 III Abstract IV 誌謝 VI 目錄 VII 符號索引 X 圖目錄 XIII 表目錄 XVI 第一章 緒論 1 1.1研究動機與目的 1 1.2文獻回顧 2 1.3論文大綱 9 第二章系統架構 12 2.1雙軸液壓泵控系統 12 2.2雙軸液壓閥控系統 16 第三章控制理論 22 3.1傳統的模糊控制器 22 3.2滑動模糊控制器 25 3.3自組織滑動模糊控制器 26 3.4適應性自組織滑動模糊控制器 30 3.5耦合適應性自組織滑動模糊控制器 32 第四章 雙軸液壓泵控系統控制器的參數設計 35 4.1前言 35 4.2 PCS學習率的設計 36 4.3 PCS適應性增益的設計 38 4.4 PCS耦合適應性增益和調節器的設計 41 第五章 雙軸液壓泵控系統不同耦合強度的實驗 44 5.1前言 44 5.2 PCS Case 1對稱負載的實驗 45 5.3 PCS Case 2中度不對稱負載的實驗 47 5.4 PCS Case 3高度不對稱負載的實驗 49 第六章 雙軸液壓閥控系統控制器的參數設計 52 6.1前言 52 6.2定閥控系統的基準 52 6.3 VCS學習率的設計 54 6.4 VCS適應性增益的設計 56 6.5 VCS耦合適應性增益和調節器的設計 59 第七章 雙軸液壓閥控系統不同耦合強度的實驗 61 7.1前言 61 7.2 VCS Case 1對稱負載的實驗 62 7.3 VCS Case 2中度不對稱負載的實驗 64 7.4 VCS Case 3高度不對稱負載的實驗 66 第八章 泵控系統與閥控系統之控制性能比較 69 8.1前言 69 8.2 Case 1 PCS與VCS對稱負載實驗的比較 69 8.3 Case 2 PCS與VCS中度不對稱負載實驗的比較 72 8.4 Case 3 PCS與VCS高度不對稱負載實驗的比較 73 第九章 結 論與未來展望 76 9.1 結論 76 9.2 未來展望 78 參考文獻 79 附錄 87 授權書 108

    參考文獻
    [1] Helduser, S., “Electric-hydrostatic drive-an innovative energy-saving power and motion control system”, Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, Vol. 213, No. 5, pp.427-437 (1999)
    [2] Kim, G.W. and Wang, K.W., “Switching sliding mode force tracking control of piezoelectric-hydraulic pump-based friction element actuation systems for automotive transmissions”, Smart Materials and Structures, Vol. 18, No.8, pp.1-15 (2009)
    [3] Wu, M.C., and Shih, M.C., “Simulated and experimental study of hydraulic anti-lock braking system using sliding-mode PWM control”, Mechatronics, Vol. 13, No. 4, pp. 331-351 (2003)
    [4] Jones, E., Dobson, A. and Roskilly, A.P., “Design of a reduced-rule self-organizing fuzzy logic controller for water hydraulic applications”, Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering, Vol. 214, No. 5, pp. 371-381 (2000)
    [5] Chiang, M. H. and Chien, Y. W., “Parallel control velocity control and energy-saving control for a hydraulic valve-controlled cylinder system using self-organizing fuzzy sliding model control”, JSME International Journal, Series C: Mechanical Systems, Machine Elements and Manufacturing, Vol. 46, No. 1, pp. 224-231 (2003)
    [6] Chu, M. H., Kang, Y., Chang, Y. F., Liu, Y. L. and Chang, C. W., “Model-following controller based on neural network for variable displacement pump”, JSME International Journal, Series C: Mechanical Systems, Machine Elements and Manufacturing, Vol. 46, No. 1, pp. 176-187 (2003)
    [7] Mohanty, A. and Yao, B., “Indirect adaptive robust control of hydraulic manipulators with accurate parameter estimates”, IEEE Transactions on Control Systems Technology, Vol. 19, No. 3, pp. 567-575 (2011)
    [8] Zadeh, L. A., “Fuzzy sets”, Information and control, Vol. 8, pp. 338-353 (1965)
    [9] Mamdani, E. H. “Application of Fuzzy Algorithms for Control Simple Dynamic Plant”, Proc. IEE, Vol. 121, No. 12, pp. 1585-1588 (1974)
    [10] Mamdani, E. H. and Assilian, S., “A Fuzzy Logic Controller for a Dynamic Plant”, Int. J. Man, Maching Study, Vol.7, pp. 1-13 (1975)
    [11] Procyk, T. J. and Mamdani, E. H. “Linguistic Self-organizing Process Controller”, Automatica, Vol. 15, No. 1, pp. 15-30 (1979)
    [12] Xu, C.-W. and Lu, Y.-Z., “Fuzzy Model Identification and Self-Learning for Dynamic Systems”. IEEE Transactions on Systems, Man and Cybernetics, Vol. SMC-17, No. 4, pp. 683-689 (1987)
    [13] Zhang, B.S. and Edmunds, J.M., “Self-Organising Fuzzy Logic Controller,” IEE Proceedings D: Control Theory and Applications, Vol.139, No.5, pp.460- 464 (1992)
    [14] Ishigame, A. Furukawa, T. Kawamoto, S. and Taniguchi, T. “Sliding mode controller design based on fuzzy inference for nonlinear systems” IEEE Transactions on Industrial Electronics, Vol. 40, No. 1, pp. 64-70 (1993)
    [15] Draper, C. S. and Li, Y. J., “Principles of optimalizing control systems and an application to an internal combustion engine”, ASME Publications (1951)
    [16] Bellman, R., Dynamic programming, Princeton University Press (1957)
    [17] Kalman, R. E., “Design of a self optimizing control system”, Transactions of ASME, pp. 468-478 (1958)
    [18] Whitaker, H.P. , Yamron, J. and Kezer, A., “Design of model-reference adaptive control systems for aircraft”, Report R-16, Instrumentation Lab., MIT (1958)
    [19] Butchart R. L. and Shackcloth, B. “Synthesis of model reference adaptive systems by Lyapunov's second method”, Proc. IFAC Symposium on Adaptive Control, 145-152 (1965)
    [20] Parks, P. C. “Lyapunov redesign of model reference adaptive control systems”, IEEE Transactions on Automatic Control, Vol. 11, pp. 362-367 (1966)
    [21] Astrom K. J.and Wittenmark, B. Adaptive control (2nd Ed.), Addison-Wesley (1995)
    [22] Yao, J. and Wang, C., “Model reference adaptive control for a hydraulic underwater manipulator, Journal of Vibration and Control”, Vol. 18, No. 6, pp. 893-902 (2012)
    [23] Barai, R. K. and Nonami, K., “Locomotion control of a hydraulically actuated hexapod robot by robust adaptive fuzzy control with self-tuned adaptation gain and dead zone fuzzy pre-compensation”, Journal of Intelligent and Robotic Systems: Theory and Applications, Vol. 53, No. 1, pp. 35-56 (2008)
    [24] Wu, H.W. and Lee, C.B., “Self-tuning adaptive speed control of a pump/inverter-controlled hydraulic motor system”, Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering, Vol. 209, No. 2, pp. 101-114 (1995)
    [25] Lin, Z. C. and Chang, D. Y., “Application of a neural network machine learning model in the selection system of sheet metal bending tooling, Artificial Intelligence in Engineering”, Vol. 10, No. 1, pp. 21-37 (1996)
    [26] Renn, J.-C. and Tsai, C., “Development of an unconventional electro-hydraulic proportional valve with fuzzy-logic controller for hydraulic presses, International Journal of Advanced Manufacturing Technology”, Vol. 26, No. 1-2, pp. 10-16 (2005)
    [27] Zheng, J. M., Zhao, S. D. and Wei, S. G., “Application of self-tuning fuzzy PID controller for a SRM direct drive volume control hydraulic press”, Control Engineering Practice, Vol. 17, No. 12, pp. 1398-1404 (2009)
    [28] Wei, S.G., Zhao, S.D., Zheng, J.M. and Zhang, Y., “Self-tuning dead-zone compensation fuzzy logic controller for a switched-reluctance-motor direct-drive hydraulic press”, Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, Vol. 223, No. 5, pp. 647-656 (2009)
    [29] Truong, D.Q. and Ahn, K.K., “Force control for press machines using an online smart tuning fuzzy PID based on a robust extended Kalman filter, Expert Systems with Applications”, Vol. 38, No. 5, pp. 5879-5894 (2011)
    [30] Lu, X. and Huang, M., “System-decomposition-based multilevel control for hydraulic press machine”, IEEE Transactions on Industrial Electronics, Vol. 59, No. 4, pp. 1980-1987 (2012)
    [31] Zhuang, M. and Atherton, D.P., “PID controller design for a TITO system, IEE Proceedings: Control Theory and Applications”, Vol. 141, No. 2, pp. 111-120 (1994)
    [32] Palmor, Z.J., Halevi, Y. and Krasney, N., “Automatic tuning of decentralized PID controllers for TITO processes”, Automatica, Vol. 31, No. 7, pp. 1001-1010 (1995)
    [33] Nordfeldt, P. and Hagglund, T., “Decoupler and PID controller design of TITO systems”, Journal of Process Control, Vol. 16, No. 9, pp. 923-936 (2006)
    [34] Jevtovic, B. T. and Matauek, M. R., “PID controller design of TITO system based on ideal decoupler”, Journal of Process Control, Vol. 20, No. 7, pp. 869-876 (2010)
    [35] Hu, Z. R., Li, D. H., Wang, J. and Xue, F., “Analytical design of PID decoupling control for TITO processes with time delays”, Journal of Computers, Vol. 6, No. 6, PP. 1064-1070 (2011)
    [36] Tomizuka, M. , Hu, J. S., Chiu, T. C. and Kamano, T., “Synchronization of two motion control axes under adaptive feedforward control”, Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, Vol. 114, No. 2, pp. 196-203 (1992)
    [37] Wang, Y. T. and Huang, C.-H., “Simplified STR applied to level control of electrohydraulic bending machine”, JSME International Journal, Series C: Dynamics, Control, Robotics, Design and Manufacturing, Vol. 39, No. 4, pp. 746-752 (1996)
    [38] Chang, Y. C., “An adaptive H∞ tracking control for a class of nonlinear multiple-input-multiple-output (MIMO) systems”, IEEE Transactions on Automatic Control, Vol. 46, No. 9, pp. 1432-1437 (2001)
    [39] Pawelczyk, M. “Multiple input-multiple output adaptive feedback control strategies for the active headrest system: Design and real-time implementation”, International Journal of Adaptive Control and Signal Processing, Vol. 17, No. 10, pp. 785-800 (2003)
    [40] Li, X. D., Xiao, T. F. and Zheng, H. X., “Adaptive discrete-time iterative learning control for non-linear multiple input multiple output systems with iteration-varying initial error and reference trajectory”, IET Control Theory and Applications, Vol. 5, No. 9, pp. 1131-1139 (2011)
    [41] Huang, S. J. and Lian, R. J., “Active vibration control of a dynamic absorber using fuzzy algorithms”, Mechatronics, Vol. 6, No. 3, pp. 317-336 (1996)
    [42] Nie, J., “Fuzzy control of multivariable nonlinear servomechanisms with explicit decoupling scheme”, IEEE Transactions on Fuzzy Systems, Vol. 5, No. 2, pp. 304-311 (1997)
    [43] Lee, T. H., Nie, J. H. and Lee, M.W., “Fuzzy controller with decoupling for multivariable nonlinear servo-mechanisms, with application to real-time control of a passive line-of-sight stabilization system”, Mechatronics, Vol. 7, No. 1, pp. 83-104, (1997)
    [44] Nie, J. and Lee, T.H., “Self-organizing rule-based control of multivariable nonlinear servomechanisms”, Fuzzy Sets and Systems, Vol. 91, No. 3, pp. 285-304, (1997)
    [45] Wang, Y. T. and Chang, M. K. “Experimental implementations of decoupling self-organizing fuzzy control to a TITO pneumatic position control system”, JSME International Journal, Series C: Dynamics, Control, Robotics, Design and Manufacturing, Vol. 42, No. 1, pp. 85-92 (1999)
    [46] Li, C. and Priemer, R., “Fuzzy control of unknown multiple-input-multiple-output plants”, Fuzzy Sets and Systems, Vol. 104, No. 2, pp. 245-267 (1999)
    [47] Lian, Ruey-Jing and Lin, Bai-Fu, “Design of a mixed fuzzy controller for multiple-input multiple-output systems” , Mechatronics, Vol. 15, No. 10, pp. 1225-1252 (2005)
    [48] Fan, S.-K.S. , Fan, C., Kung, P. and Wang, C.-Y., “Development of Run-To-Run (R2R) controller for the multiple-input multiple-output (MIMO) system using fuzzy control theories”, International Journal of Production Research, Vol. 45, No. 14, pp. 3215-3243 (2007)
    [49] 王進德、蕭大全,類神經網路與模糊控制理論入門,全華科技圖書股份有限公司,台北,第192-199頁(2005)
    [50] 楊清任,「即時語意式自組織模糊控制器之設計」,碩士論文,國立台灣大學,台北(1992)
    [51] Chang, Ming-Kun “An adaptive self-organizing fuzzy sliding mode controller for a 2-DOF rehabilitation robot actuated by pneumatic muscle actuators”, Control Engineering Practice, Vol. 18, pp.13-22 (2010)

    QR CODE