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T-S model based indirect adaptive fuzzy control using online parameter estimation.

Chang-Woo Park1, Young-Wan Cho

  • 1Precision Machinery Research Center, Korea Electronics Technology Institute, Kyunggi-Do 420-140, Korea. drcwpark@keti.re.kr

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|December 29, 2004
PubMed
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This study introduces an adaptive parameter estimation scheme for Takagi-Sugeno fuzzy models, enabling robust control systems that adapt to system changes. The method ensures controllers work effectively despite parameter variations.

Area of Science:

  • Control Engineering
  • Fuzzy Systems
  • Adaptive Control

Background:

  • Takagi-Sugeno (T-S) fuzzy models are widely used in control systems.
  • Parameter perturbation in these models can degrade control performance.
  • Adaptive control strategies are needed to maintain robustness.

Purpose of the Study:

  • To design and analyze a parameter estimation scheme for general MIMO T-S fuzzy models.
  • To develop an adaptive law for online parameter updating.
  • To demonstrate the effectiveness of the proposed estimator in adaptive fuzzy control.

Main Methods:

  • Lyapunov theory-based analysis for stability and convergence.
  • Online parameter estimation by observing system behavior.
  • Integration with an existing fuzzy state feedback controller for indirect adaptive control.

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Main Results:

  • The designed adaptive law effectively estimates parameters of T-S fuzzy models.
  • The online parameter estimator allows fuzzy controllers to adapt to parameter perturbations.
  • Numerical simulations and experiments validate the estimator's performance and the control system's robustness.

Conclusions:

  • The proposed parameter estimation scheme enhances the adaptability and robustness of fuzzy control systems.
  • The adaptive law ensures accurate parameter tracking for parameterized plant models.
  • This approach is applicable to various fuzzy controllers and system parameter variations.