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研究生:吳鴻璋
研究生(外文):Hung-Chang Wu
論文名稱:強健模糊類神經控制器設計
論文名稱(外文):A Robust Fuzzy Neural Controller Design
指導教授:蔡樸生
指導教授(外文):Pu-Sheng Tsai
學位類別:碩士
校院名稱:中華技術學院
系所名稱:電子工程研究所碩士班
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:中文
論文頁數:77
中文關鍵詞:滑動模式適應控制小腦控制器模糊控制系統穩定
外文關鍵詞:sliding modeadaptive controlCMAC controllerfuzzy controlsystem stability
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本論文針對非線性系統,提出一強健模糊類神經控制器,此控制器結合了模糊系統、類神經小腦模型及滑動模式達成閉迴路控制系統的穩定性要求。在小腦模型中,透過模糊規則的建立,獲得模糊小腦控制器。首先,將小腦權重記憶體引入模糊控制規則的後件部後,以獲得一參數化描述的模糊小腦控制器,為了能夠即時反應系統動態行為,利用適應控制的設計技巧,修正小腦控制器的權重參數,以保證模糊小腦控制系統的穩定性。同時藉由參數投影法,局限參數在所指定的有界範圍內達成權重參數的收斂性,進而限制了控制器輸出是有界及可實現的。最後,將所提之控制器應用於行星齒輪式倒單擺系統、電子驅動系統,以驗證所提之控制器的性能。
A robust fuzzy neural controller for a class of uncertain nonlinear dynamical systems is proposed and analyzed. The controller combines fuzzy system, CMAC and sliding mode, achieve the stable of closed loop system. The adaptive fuzzy CMAC incorporates the CMAC into the THEN-Part of fuzzy system, so that a simplified fuzzy CMAC can be achieved. The weights of CMAC as well as the parameters of fuzzy system can be tuned in real time operation. The system stability and parameters convergence of fuzzy CMAC can be guaranteed with Lyapunov stability analysis. Finally, two nonlinear systems, a planetary train type inverted pendulum system and an electric drive system are respectively utilized to prove the satisfactory performances of the proposed control scheme.
中文摘要 i
英文摘要 ii
目錄 iii
表圖目錄 iv
第一章 緒論 1
第一節 研究動機與背景 1
第二節 文獻回顧 5
第三節 論文架構 11
第二章 理論背景 13
第一節 模糊控制 13
第二節 小腦控制 15
第三節 二維小腦模型控制器 19
第四節 模糊小腦控制器 23
第三章 控制器設計 27
第一節 系統描述 29
第二節 理想控制器設計 30
第三節 強健類神經適應控制器設計 32
第四章 模擬結果 40
第五章 結論 56
參考文獻 57
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