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Robust fuzzy filter design for nonlinear systems with persistent bounded disturbances.

Chung-Shi Tseng

    IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
    |August 15, 2006
    PubMed
    Summary
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    This study presents a novel fuzzy filtering approach for nonlinear dynamic systems, effectively addressing persistent bounded disturbances. The method minimizes estimation error bounds using linear matrix inequality (LMI) optimization.

    Area of Science:

    • Control Systems Engineering
    • Fuzzy Logic Systems
    • Nonlinear Dynamics

    Background:

    • Conventional methods fail to solve nonlinear Linfinity-gain filtering problems for systems with persistent bounded disturbances.
    • The Takagi and Sugeno fuzzy model is utilized to approximate complex nonlinear dynamic systems.

    Discussion:

    • A fuzzy filter is designed to minimize the Linfinity-gain of the estimation error under linear matrix inequality (LMI) constraints.
    • The nonlinear filtering problem is reformulated as a suboptimal filtering problem solvable via LMI-based optimization.

    Key Insights:

    • The proposed fuzzy filtering design effectively attenuates estimation error peaks caused by persistent bounded disturbances.
    • This approach extends Linfinity-gain filtering capabilities from linear to nonlinear dynamic systems.

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    Outlook:

    • Further research can explore adaptive fuzzy filtering strategies for enhanced robustness.
    • Investigating the application of these fuzzy filters in real-world complex systems is a potential future direction.