| 標題：Supervisory adaptive dynamic RBF-based neural-fuzzy control system design for unknown nonlinear systems |
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| 學年 | 101 |
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| 學期 | 2 |
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| 出版（發表）日期 | 2013/04/01 |
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| 作品名稱 | Supervisory adaptive dynamic RBF-based neural-fuzzy control system design for unknown nonlinear systems |
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| 作品名稱（其他語言） | |
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| 著者 | Hsu, Chun-Fei; Lin, Chih-Min; Yeh, Rong-Guan |
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| 單位 | 淡江大學電機工程學系 |
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| 出版者 | Amsterdam: Elsevier BV |
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| 著錄名稱、卷期、頁數 | Applied Soft Computing 13(4), pp.1620-1626 |
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| 摘要 | Many published papers show that a TSK-type fuzzy system provides more powerful representation than a Mamdani-type fuzzy system. Radial basis function (RBF) network has a similar feature to the fuzzy system. As this result, this article proposes a dynamic TSK-type RBF-based neural-fuzzy (DTRN) system, in which the learning algorithm not only online generates and prunes the fuzzy rules but also online adjusts the parameters. Then, a supervisory adaptive dynamic RBF-based neural-fuzzy control (SADRNC) system which is composed of a DTRN controller and a supervisory compensator is proposed. The DTRN controller is designed to online estimate an ideal controller based on the gradient descent method, and the supervisory compensator is designed to eliminate the effect of the approximation error introduced by the DTRN controller upon the system stability in the Lyapunov sense. Finally, the proposed SADRNC system is applied to control a chaotic system and an inverted pendulum to illustrate its effectiveness. The stability of the proposed SADRNC scheme is proved analytically and its effectiveness has been shown through some simulations. |
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| 關鍵字 | Adaptive control;Sliding-mode control;Neural-fuzzy system;Chaotic system;Inverted pendulum |
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| 語言 | 英文 |
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| ISSN | 1872-9681 |
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| 期刊性質 | 國外 |
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| 收錄於 | SCI; |
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| 產學合作 | |
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| 通訊作者 | |
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| 審稿制度 | 是 |
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| 國別 | 荷蘭 |
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| 公開徵稿 | |
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| 出版型式 | ,電子版,紙本 |
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| 相關連結 | |
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