An Online GA-Based Output-Feedback Direct Adaptive Fuzzy-Neural Controller for Uncertain Nonlinear Systems

Wei Yen Wang, Chih Yuan Cheng, Yih Guang Leu

研究成果: 雜誌貢獻期刊論文同行評審

97 引文 斯高帕斯(Scopus)

摘要

In this paper, we propose a novel design of a GA-based output-feedback direct adaptive fuzzy-neural controller (GODAF controller) for uncertain nonlinear dynamical systems. The weighting factors of the direct adaptive fuzzy-neural controller can successfully be tuned online via a GA approach. Because of the capability of genetic algorithms (GAs) in directed random search for global optimization, one is used to evolutionarily obtain the optimal weighting factors for the fuzzy-neural network. Specifically, we use a reduced-form genetic algorithm (RGA) to adjust the weightings of the fuzzy-neural network. In RGA, a sequential-search -based crossover point (SSCP) method determines a suitable crossover point before a single gene crossover actually takes place so that the speed of searching for an optimal weighting vector of the fuzzy-neural network can be improved. A new fitness function for online tuning the weighting vector of the fuzzy-neural controller is established by the Lyapunov design approach. A supervisory controller is incorporated into the GODAF controller to guarantee the stability of the closed-loop nonlinear system. Examples of nonlinear systems controlled by the GODAF controller are demonstrated to illustrate the effectiveness of the proposed method.

原文英語
頁(從 - 到)334-345
頁數12
期刊IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
34
發行號1
DOIs
出版狀態已發佈 - 2004 二月

ASJC Scopus subject areas

  • 控制與系統工程
  • 軟體
  • 資訊系統
  • 人機介面
  • 電腦科學應用
  • 電氣與電子工程

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