English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 51296/86402 (59%)
造訪人次 : 8169303      線上人數 : 51
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: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/98858


    題名: On-Line Genetic Algorithm-Based Fuzzy-Neural Sliding Mode Controller Using Improved Adaptive Bound Reduced-Form Genetic Algorithm
    作者: Lin, Ping-Zong;Wang, Wei-Yen;Lee, Tsu-Tian;Wang, Chi-Hsu
    貢獻者: 淡江大學電機工程學系
    關鍵詞: fuzzy-neural sliding mode controller;adaptive bound reduced-form genetic algorithm;robot manipulator;on-line genetic algorithm-based controller
    日期: 2009-06-01
    上傳時間: 2014-09-24 09:50:56 (UTC+8)
    出版者: Abingdon: Taylor & Francis
    摘要: In this article, a novel on-line genetic algorithm-based fuzzy-neural sliding mode controller trained by an improved adaptive bound reduced-form genetic algorithm is developed to guarantee robust stability and good tracking performance for a robot manipulator with uncertainties and external disturbances. A general sliding manifold, which can be non-linear or time varying, is used to construct a sliding surface and reduce control law chattering. In this article, the sliding surface is used to derive a genetic algorithm-based fuzzy-neural sliding mode controller. To identify structured system dynamics, a B-spline membership function fuzzy-neural network, which is trained by the improved genetic algorithm, is used to approximate the regressor of the robot manipulator. The sliding mode control with a general sliding surface plays the role of a compensator when the fuzzy-neural network does not approximate the dynamics regressor of the robot manipulator well in the transient period. The adjustable parameters of the fuzzy-neural network are tuned by the improved genetic algorithm, which, with the use of the sequential-search-based crossover point method and the single gene crossover, converges quickly to near-optimal parameter values. Simulation results show that the proposed genetic algorithm-based fuzzy-neural sliding mode controller is effective and yields superior tracking performance for robot manipulators.
    關聯: International Journal of Systems Sicence 40(6), pp.571-585
    DOI: 10.1080/00207720902750011
    顯示於類別:[電機工程學系暨研究所] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    index.html0KbHTML95檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

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