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研究生:楊清雲
研究生(外文):Ching-Yun Yang
論文名稱:針對未知非線性系統之強健參數小腦模型控制器設計
論文名稱(外文):ROBUST PARAMETRIC CEREBELLAR MODEL ARTICULATION CONTROLLER DESIGN FOR UNKNOWN NONLINEAR SYSTEMS
指導教授:呂虹慶
指導教授(外文):Hung-Ching Lu
學位類別:碩士
校院名稱:大同大學
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:60
中文關鍵詞:小腦模型控制器參數小腦模型控制器未知非線性系統
外文關鍵詞:CMACP-CMACunknown nonlinear systems
相關次數:
  • 被引用被引用:3
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  • 下載下載:30
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針對未知非線性系統,本文提出強健參數小腦模型控制器(RP-CMAC)設計。對於平滑輸入的非平滑響應和對於複雜系統有不良的代表能力是小腦模型控制器現存的問題,強健參數小腦模型控制器有解決這些問題的能力。強健參數小腦模型控制器是由參數小腦模型控制器(P-CMAC)和強健控制器(robust controller)所組成,而參數小腦模型控制器是用來近似理想控制器。其中,小腦模型控制器(CMAC)和Takagi-Sugeno-Kang 參數模糊推論(TSK Fuzzy)所組成參數小腦模型控制器的權重是根據里亞普諾夫(Lyapunov) 函數求得的適應法則來作線上的調整,而強健控制器的設計是用來保證強健追蹤的效能。此外並應用滑動模式控制的概念於控制器設計中。由於滑動面有較快的暫態響應,本文採用滑動面做為所提出RP-CMAC的輸入空間,因此所提出的控制器有更多的強健性來改善不確定性和近似誤差。最後,將所提出的控制器應用於一個倒單擺系統,由模擬與比較的結果可以印證所提出的控制器具有較好的追蹤效能。
This thesis is to propose the design of robust parametric cerebellar model arithmetic controller (RP-CMAC) for unknown nonlinear systems. Since the non-smooth response for smooth inputs and the poor representation capabilities for complex systems are the existing problems of CMAC algorithm, the RP-CMAC is able to solve them. The RP-CAMC combines with a P-CMAC and a robust controller, and P-CMAC is utilized to approximate an ideal controller. The weights of the P-CMAC which is comprised of CMAC and Takagi-Sugeno-Kang parametric fuzzy inference (TSK Fuzzy) are tuned on-line by the derived adaptive law based on the Lyapunov function. For guaranteeing a specified robust tracking performance, the robust controller is designed and furthermore, the concept of sliding-mode control (SMC) is adopted in the control scheme. The sliding surface is adopted as the input space of the proposed RP-CMAC due to its fast transient response. Thus, the proposed control scheme has more robustness against the uncertainties and the approximated error, and then, it is applied to control the inverted pendulum system. Simulation results illustrate that the proposed control scheme has better tracking performance.
ACKNOWLEDGEMENTS I
ABSTRACT (IN ENGLISH) II
ABSTRACT (IN CHINESE) III
TABLES OF CONTENTS IV
LIST OF TABLES V
LIST OF FIGURES VI
CHAPTER
1 INTRODUCTION 1
2 CMAC AND P-CMAC NETWORTS 5
2.1 CMAC Neural Networks 5
2.1.1 One-Dimensional CMAC 6
2.1.2 Two-Dimensional CMAC 8
2.2 Two-Dimensional CMAC with Gaussian Basis Function 11
2.3 Parametric CMAC Neural Networks 13
3 ROBUST CONTROL SYSTEM DESIGN 17
3.1 Problem Statement 17
3.2 RP-CMAC Design 19
4 SIMULATION RESULTS 24
5 CONCLUSIONS 57
REFERNECES 58
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