| 標題：Adaptive Control for Mimo Uncertain Nonlinear Systems Using Recurrent Wavelet Neural Network |
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| 學年 | 100 |
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| 學期 | 2 |
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| 出版（發表）日期 | 2012/02/01 |
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| 作品名稱 | Adaptive Control for Mimo Uncertain Nonlinear Systems Using Recurrent Wavelet Neural Network |
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| 作品名稱（其他語言） | |
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| 著者 | Lin, Chih-Min; Ting, Ang-Bung; Hsu, Chun-Fei; Chung, Chao-Ming |
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| 單位 | 淡江大學電機工程學系 |
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| 出版者 | Singapore: World Scientific Publishing Co. Pte. Ltd. |
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| 著錄名稱、卷期、頁數 | International Journal of Neural Systems 22(1), pp.37-50 |
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| 摘要 | Recurrent wavelet neural network (RWNN) has the advantages such as fast learning property, good generalization capability and information storing ability. With these advantages, this paper proposes an RWNN-based adaptive control (RBAC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The RBAC system is composed of a neural controller and a bounding compensator. The neural controller uses an RWNN to online mimic an ideal controller, and the bounding compensator can provide smooth and chattering-free stability compensation. From the Lyapunov stability analysis, it is shown that all signals in the closed-loop RBAC system are uniformly ultimately bounded. Finally, the proposed RBAC system is applied to the MIMO uncertain nonlinear systems such as a mass-spring-damper mechanical system and a two-link robotic manipulator system. Simulation results verify that the proposed RBAC system can achieve favorable tracking performance with desired robustness without any chattering phenomenon in the control effort. |
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| 關鍵字 | Wavelet neural network;adaptive control;nonlinear system;uniformly ultimately bounded |
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| 語言 | 英文 |
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| ISSN | 1793-6462 |
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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 | Lin, Chih-Min; Hsu, Chun-Fei |
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| 審稿制度 | 是 |
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| 國別 | 新加坡 |
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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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