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

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageAmerican English
Pages (from-to)3669-3674
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume4
DOIs
StatePublished - 24 Nov 2003
EventSystem Security and Assurance - Washington, DC, United States
Duration: 5 Oct 20038 Oct 2003

Keywords

  • Adaptive controller
  • Lease-squared identification
  • Sampling time
  • Takagi-Sugeno(TS)-type FNN
  • Tracking

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