Robust adaptive fuzzy-neural controllers for uncertain nonlinear systems

研究成果: 雜誌貢獻期刊論文同行評審

147 引文 斯高帕斯(Scopus)


A robust adaptive fuzzy-neural controller for a class of unknown nonlinear dynamic systems with external disturbances is proposed in this paper. The fuzzy-neural approximator is established to approximate an unknown nonlinear dynamic system in a linearized way. The fuzzy B-spline membership function (BMF) which possesses fixed number of control points is developed for on-line tuning. The concept of tuning the adjustable vectors, which include membership functions and weighting factors, is described to derive the update laws of the robust adaptive fuzzy-neural controller. Furthermore, the effect of all the unmodeled dynamics, BMF modeling errors and external disturbances on the tracking error is attenuated by the error compensator which is also constructed by the fuzzy-neural inference. In this paper, we can prove that the closed-loop system which is controlled by the robust adaptive fuzzy-neural controller is stable and the tracking error will converge to zero under mild assumptions. Several examples are simulated in order to confirm the effectiveness and applicability of the proposed methods in this paper.

頁(從 - 到)805-817
期刊IEEE Transactions on Robotics and Automation
出版狀態已發佈 - 1999

ASJC Scopus subject areas

  • 控制與系統工程
  • 電氣與電子工程


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