(3.236.231.14) 您好!臺灣時間:2021/04/15 07:09
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:丁輔善
研究生(外文):Fu-Shan Ting
論文名稱:以小腦模型為基底之適應性控制器設計及其應用
論文名稱(外文):Cerebellar Model Articulation Controller-Based Adaptive Control System Design and Its Applications
指導教授:林志民林志民引用關係
指導教授(外文):Chih-Min Lin
學位類別:碩士
校院名稱:元智大學
系所名稱:電機工程學系
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:69
中文關鍵詞:適應性強健型小腦模型單輸入單輸出的高壓電力供應系統混沌系統
外文關鍵詞:adaptive robust cerebellar model articulationsingle-input single-output (SISO) high voltage power supply (HVPS) systemchaotic system
相關次數:
  • 被引用被引用:0
  • 點閱點閱:152
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本論文之主旨係在於發展適應性強健型小腦模型控制器,其並結合適應性控制、與強健控制等理論,再依據李雅普諾夫穩定性定理設計小腦模型控制器的參數適應性調整法則,因此整個閉迴路控制系統的穩定性可以被保證。最後並廣泛的應用在一些具有非線性且不確定系統之閉迴路控制上。本論文將探討所提出來的適應性強健型小腦模型控制器及其應用性。首先,將介紹小腦模型控制器,接著具有最佳學習速率的小腦模型控制器將針對單輸入-單輸出的高壓電力供應系統。在多輸入-多輸出控制系統方面,本論文針對系統分別提出不需數學模型及需要數學模型之控制法則。這些控制法則分別採用強健型小腦模型控制器為主控制器,所開發出來的多輸入-多輸出適應性強健型小腦模型控制器並應用於解決一些具有高度非線性且時變系統的軌跡追蹤問題,例如混沌系統。經由模擬與實作的結果顯示,對於這些具有不確定量且非線性之系統,本論文所提出的控制系統均能達到令人滿意的控制性能。
The purpose of this thesis is to develop the adaptive robust cerebellar model articulation controller (CMAC) system by integrating CMAC with adaptive control and robust control technologies for the control application to uncertain nonlinear systems. According to Lyapunov synthesis approach, the adaptive tuning laws of CMAC can be derived and the system stability can be guaranteed. This thesis introduces the structures of CMAC first. Then, CMAC with optimal learning rate is developed for the single-input single-output (SISO) high voltage power supply (HVPS) system. Moreover, this thesis also proposes the robust CMAC control systems for the uncertain nonlinear MIMO systems. In this design, the developed multi-input multi-output (MIMO) control system is then applied to a nonlinear chaotic system. From the simulation results, the control schemes proposed in this thesis have been shown to achieve satisfactory control performance for the considered nonlinear systems.
書名頁 i
論文口試委員審定書 ii
授權書 iii
摘要 iv
Abstract v
致謝 vi
Contents vii
List of Tables ix
List of Figures x
Nomenclature xiii

Chapter 1 Introduction 1

Chapter 2 Adaptive CMAC Design with Optimal Learning Rate
for High Voltage Power Supply
2.1 Introduction of CMAC 3
2.2 Controller design of power converter 8
2.2.1 Problem formulation
2.2.2 General cerebella model articulation neural network 9
2.2.3 Learning algorithm 11
2.3 Stability analysis 13
2.4 HVPS Model 19
2.4.1 Buck DC-DC converter model 19
2.4.2 Modeling of high voltage power converter 22
2.4.3 Transfer function of PWM 24
2.5 Simulation result for HVPS 24

Chapter 3 Robust CMAC Control for MIMO Nonlinear Systems
3.1 Problem formulation 38
3.2 Robust controller design 40
3.3 Simulation result 47

Chapter 4 Conclusion and Suggestion for Future Research
4.1 Conclusions 62
4.2 Suggestion for future research 62
Reference 64
Autobiography 72
[1]L. X. Wang, Adaptive Fuzzy Systems and Control: Design and Stability Analysis, Englewood Cliffs, NJ: Prentice-Hall, 1994.
[2]Y. G. Leu, T. T. Lee, and W. Y. Wang, “On-line tuning of fuzzy-neural network for adaptive control of nonlinear dynamical systems,” IEEE Trans. Syst., Man, Cybern. B, vol. 27, no. 6, pp. 1034-1043, 1997.
[3]C. H. Wang, T. C. Lin, T. T. Lee, and H. L. Liu, “Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems,” IEEE Trans. Syst., Man, Cybern. B, vol. 32, no. 5, pp. 583-597, 2002.
[4]J. Y. Chen, P. S. Tsai, and C. C. Wong, “Adaptive design of a fuzzy cerebellar model arithmetic controller neural network,” IEE, Contr. Theory Appl., vol. 152, no. 2, pp. 133-137, 2005.
[5]C. M. Lin and Y. F. Peng, “Adaptive CMAC-based supervisory control for uncertain nonlinear systems,” IEEE Trans. Syst., Man, Cybern. B, vol. 34, no. 2, pp. 1248-1260, 2004.
[6]J. H. Park, S. H. Huh, S. H. Kim, S. J. Seo, and G. T. Park, “Direct adaptive controller for nonaffine nonlinear systems using self-structuring neural networks,” IEEE Trans. Neural Networks, vol. 16, no. 2, pp. 414-422, 2005.
[7]B. S. Chen, C. H. Lee, and Y. C. Chang, “ tracking design of uncertain nonlinear SISO systems: Aadaptive fuzzy approach,” IEEE Trans. Fuzzy Systems, vol. 4, no. 1, pp. 32-43, 1996.
[8]W. Y. Wang, M. L. Chan, C. C. J. Hsu, and T. T. Lee, “ tracking-based sliding mode control for uncertain nonlinear systems via an adaptive fuzzy-neural approach,” IEEE Trans. Syst., Man, Cybern. B, vol. 32, no. 4, pp. 483-492, 2002.
[9]S. Tong, H. X. Li, and W. Wang, “Observer-based adaptive fuzzy control for SISO nonlinear systems,” Fuzzy Sets Syst., vol. 148, no. 3, pp. 355-376, 2004.
[10]C. M. Lin, Y. F. Peng, and C. F. Hsu, “Robust cerebellar model articulation controller design for unknown nonlinear systems,” IEEE Trans. Circuits Syst. II, vol. 51, no. 7, pp. 354-358, 2004.
[11]J. S. Albus, “A new approach to manipulator control: The cerebellar model articulation controller (CMAC),” Trans. ASME, J. Dyn. Syst. Meas. Control, vol. 97, no. 3, pp. 220-227, 1975.
[12]S. H. Lane, D. A. Handelman, and J. J. Gelfand, “Theory and development of higher-order CMAC neural networks,” IEEE Control Syst. Mag., vol. 12, no. 2, pp. 23-30, 1992.
[13]J. C. Jan and S. L. Hung, “High-order MS_CMAC neural network,” IEEE Trans. Neural Networks, vol. 12, no. 3, pp. 598-603, 2001.
[14]R. J. Wai, C. M. Lin, and Y. F. Peng, “Robust CMAC neural network control for LLCC resonant driving linear piezoelectric ceramic motor,” Proc. IEE, Contr. Theory Appl., vol. 150, no. 3, pp. 221-232, 2003.
[15]Y. F. Peng, R. J. Wai, and C. M. Lin, “Implementation of LLCC-resonant driving circuit and adaptive CMAC neural network control for linear piezoelectric ceramic motor,” IEEE Trans. Ind. Electron., vol. 51, no. 1, pp. 35-48, 2004.
[16]C. T. Chiang and C. S. Lin, “CMAC with general basis functions,” Neural Networks, vol. 9, no. 7, pp. 1199-1211, 1996.
[17]C. M. Lin and Y. F. Peng, “Missile guidance law design using adaptive cerebellar model articulation controller,” IEEE Trans. Neural Networks, vol. 16, no. 3, pp. 636-644, 2005.
[18]Y. H. Kim and F. L. Lewis, “Optimal design of CMAC neural-network controller for robot manipulators,” IEEE Trans. Syst., Man, Cybern. C, vol. 30, pp. 22–31, Feb. 2000.
[19]S. Jagannathan, “Discrete-time CMAC NN control of feedback linearizable nonlinear systems under a persistence of excitation,” IEEE Trans. Neural Networks, vol. 10, pp. 128–137, Jan. 1999.
[20]C. C. Ku and K. Y. Lee, “Diagonal recurrent neural networks for dynamic systems control,” IEEE Trans. Neural Networks, vol. 6, pp. 144–156, Jan. 1995.
[21]H. J. Uang and B. S. Chen, “Robust adaptive optimal tracking design for uncertain missile systems: a fuzzy approach,” Fuzzy Sets and Syst., vol. 126, pp. 63-87, Feb. 2002.
[22]I. Rhee and J.L. Speyer, “A game theoretic approach to a finite-time disturbance attenuation problem,” IEEE Trans. Automat. Control vol. 36 pp. 1021–1032. Sep. 1991.
[23]A. Stoorvogel, The Control Problem: A State Approach, Prentice-Hall, Englewood Cli7s, NJ, 1992.
[24]D. Marr, “A theory of cerebellar cortex,” J. Physiol., vol. 202, pp. 437-470, 1969.
[25]J. S. Albus, “A theory of cerebellar function,” Math. Biosci., vol. 10, pp. 25-61, 1971.
[26]D. J. Linden, “Cerebellar long-term depression as investigated in a cell culture preparation,” Behav. Brain Sci., vol. 19, pp. 339-346, 1996.
[27]P. Chauvet and G. A. Chauvet, “Mathematical conditions for adaptive control in Marr’s model of the sensorimotor system,” Neural Networks, vol. 8, no. 5, pp. 693-706, 1995.
[28]M. Ito, The Cerebellum and Neural Control. Raven Press, New York, 1984.
[29]D. O. Hebb, The Organization of Behavior: A Neuropsychological Theory. New York: Wiley, 1949.
[30]M. Ito, “Mechanisms of motor learning in the cerebellum,” Brain Research Interactive, vol. 886, pp. 237-245, 2000.
[31]L. Sivan, Microware Tube Transmitters, London, Chapman & Hall, 1994.
[32]A. S. Gilmour, Principles of Traveling Wave Tubes, Norwood, Artech House, 1994.
[33]A. I. Pressman, Switching Power Supply Design, Singapore, McGraw-Hill, 1992.
[34]C. Iannello, S. Luo, and I. Batarseh, “Small-signal and transient analysis of a full-bridge, zero-current-switched PWM converter using an average,” IEEE Trans. Power Electron, vol. 18, no. 3, pp. 793-801, 2003.
[35]V. S. C. Raviraj and P. C. Sen, “Comparative study of proportional-integral, sliding mode and fuzzy logic controllers for power converters,” IEEE Trans. Ind. Appl., vol. 33, no. 2, pp. 518-524, 1997.
[36]A. Diordiev, O. Ursaru, M. Lucanu, and L. Tigaeru, “A hybrid PID-fuzzy controller for DC-DC converters,” International Symposium on Signals, Circuits, and Systems, vol. 1, pp. 97-100, 2003
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關論文
 
1. 邱上真(1996)。落實全面性身心障礙教育—回應黃委員榮村所提教育改革研討會會議報告。教改通訊,19,40-44。
2. 林燕玲(2004)。談身心障礙學生國民義務教育階段後之升學轉銜服務。特教園丁,19(3),43-48。
3. 余杏容(1977)。離職相關因素之探討。思與言,15(2),99-105。
4. 王明仁、曾鈺惠(1996)。學校社會工作的理論、模式和推展。社區發展季刊,73,5-14。
5. 12.曾芳美(2005),「TFT-LCD大尺寸面板廠商企業價值之評估:以友達光電為例」,產業論壇,第七卷第一期,頁72-94。
6. 6.林嬋娟、吳安妮(1992),台灣企業併購綜效及績效之實證研究,會計評論,第26期。
7. 4.余尚武、江玉柏,「影響企業購併成敗之因素與策略探討」,《經濟情勢暨評論》,經濟部研發會,第四卷第二期,頁125-144。
8. 宣崇慧(2001)。資源教室方案理念在大專院校之實踐。特殊教育季刊,78,20-25。
9. 張蓓莉(1998)。資源教室方案應提供的支援服務。特殊教育季刊,67,1-5。
10. 萬明美、張照明、陳麗君(1997)。大學視覺障礙學生學校生活適應及大學同儕對其態度之研究。特殊教育學報,12,1-39。
11. 葉肅科(2002)。身心障礙者福利與人權保障。社區發展季刊,99,363-377。
12. 嚴嘉楓、林金定(2003)。身心障礙者人權與福利政策發展。身心障礙研究,1(1),20-31。
 
系統版面圖檔 系統版面圖檔