English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 61754/94641 (65%)
造訪人次 : 1634407      線上人數 : 19
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/75381

    題名: Adaptive Control for Mimo Uncertain Nonlinear Systems Using Recurrent Wavelet Neural Network
    作者: Lin, Chih-Min;Ting, Ang-Bung;Hsu, Chun-Fei;Chung, Chao-Ming
    貢獻者: 淡江大學電機工程學系
    關鍵詞: Wavelet neural network;adaptive control;nonlinear system;uniformly ultimately bounded
    日期: 2012-02-01
    上傳時間: 2012-03-22 14:36:28 (UTC+8)
    出版者: Singapore: World Scientific Publishing Co. Pte. Ltd.
    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.
    關聯: International Journal of Neural Systems 22(1), pp.37-50
    DOI: 10.1142/S0129065712002992
    顯示於類別:[電機工程學系暨研究所] 期刊論文


    檔案 描述 大小格式瀏覽次數
    S0129065712002992.pdfpaper520KbAdobe PDF313檢視/開啟



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋