On-line intelligent adaptive control for uncertain nonlinear systems using TS-type fuzzy models with maximum allowable computational time for controller

Chi-Hsu Wang*, Shi Hao Ker, Han Leih Liu, Tsu Tian Lee

*此作品的通信作者

研究成果: Conference article同行評審

摘要

A new Takagi-Sugeno(TS)-type FNN learning architecture is proposed for the on-line identification of the TS-type fuzzy mode! of the uncertain system. The dynamical optimal learning rule is adopted to update the linearized TS-type fuzzy model to guarantee the convergence of on-line training process. To improve the convergence speed of the on-line training process, the lease-squared identification is applied to identify the initial parameters of the TS-type fuzzy model. Once the linearized TS-type fuzzy model of the uncertain nonlinear system is obtained in real-time environment, the on-line adaptive controller can be easily designed to accomplish the design specifications. A simplified tracking controller is also proposed to perform the tracking of a reference signal for unknown system. Critical constraint criteria are applied to find the computational time for generating controller signal Based on this sampling time, suitable equipments are used in actual hardware implementation. Inverted pendulum system is illustrated to track sinusoidal signal.

原文American English
頁(從 - 到)3669-3674
頁數6
期刊Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
4
DOIs
出版狀態Published - 2003
事件System Security and Assurance - Washington, DC, United States
持續時間: 5 10月 20038 10月 2003

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