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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
Summary
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.
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.