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Hybrid compensation control for affine TSK fuzzy control systems.

Chih-Ching Hsiao1, Shun-Feng Su, Tsu-Tian Lee

  • 1Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106 Taiwan, ROC.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 7, 2004
PubMed
Summary

This study introduces a novel state feedback controller design for Takagi-Sugeno-Kang (TSK) fuzzy models. The method ensures desired control performance by individually compensating for fuzzy rule variations, improving stability and predictability.

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Area of Science:

  • Control Systems Engineering
  • Fuzzy Logic Systems
  • Nonlinear Control Theory

Background:

  • Designing state feedback controllers for affine Takagi-Sugeno-Kang (TSK) fuzzy models presents challenges in ensuring stability and performance, especially when fuzzy rules vary widely.
  • Existing robust control approaches that treat rule variations collectively can lead to unsatisfied stability conditions and unpredictable closed-loop system performance.

Purpose of the Study:

  • To propose a new state feedback controller design methodology for affine TSK fuzzy models.
  • To develop a controller that compensates for all fuzzy rules individually to achieve desired overall system performance.
  • To overcome limitations of previous approaches that may fail to satisfy stability conditions for widely distributed fuzzy rules.

Main Methods:

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  • Combining two distinct control design methodologies to create a novel controller.
  • Treating each fuzzy rule as a variation of a nominal rule.
  • Individually analyzing and compensating for rule variations using a Lyapunov stability approach.

    • Main Results:

    • The proposed controller design effectively compensates for all fuzzy rules, ensuring desired control performance.
    • The individual treatment of rule variations in a Lyapunov sense enhances stability conditions, even with wide rule distribution.
    • Demonstrated effectiveness through various simulation examples, illustrating good control performances.

    Conclusions:

    • The proposed method offers a more predictable and stable approach to designing state feedback controllers for TSK fuzzy models compared to robust control methods.
    • This approach ensures that desired control performance is achieved across the entire operating range of the fuzzy system.
    • The individual compensation strategy is crucial for maintaining stability and performance when dealing with significant variations in fuzzy rules.