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Robust Fault Detection for Switched Fuzzy Systems With Unknown Input.

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    This study introduces a new observer for fault detection in switched nonlinear systems, effectively removing unknown inputs for robust and sensitive performance. The method enhances system reliability through advanced fuzzy logic techniques.

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    Area of Science:

    • Control Systems Engineering
    • Nonlinear System Analysis
    • Fuzzy Logic Applications

    Background:

    • Switched nonlinear systems present challenges in fault detection due to their complex dynamics.
    • Unknown inputs can significantly obscure fault signals, hindering accurate detection.
    • Existing fault detection methods may lack robustness or sensitivity in the presence of uncertainties.

    Purpose of the Study:

    • To develop a novel fault detection method for switched nonlinear systems under the Takagi-Sugeno (T-S) fuzzy framework.
    • To design an unknown input observer that can effectively eliminate the influence of unknown inputs on fault detection residuals.
    • To enhance the robustness and sensitivity of the fault detection scheme using a weighted performance level.

    Main Methods:

    • Design of a T-S fuzzy-based unknown input observer for switched nonlinear systems.
    • Incorporation of a weighted performance level to balance robustness and sensitivity.
    • Development of a fault detection residual generation strategy that removes unknown input effects.

    Main Results:

    • The proposed observer successfully removes unknown inputs from the fault detection residual.
    • The weighted performance level enhances both the robustness against disturbances and the sensitivity to faults.
    • Simulations on a numerical example and an electromechanical system validate the effectiveness of the proposed scheme.

    Conclusions:

    • The novel fault detection observer provides a robust and sensitive solution for switched nonlinear systems.
    • The method effectively addresses the challenge of unknown inputs in fault detection.
    • The T-S fuzzy framework combined with the weighted performance level offers a promising approach for reliable system monitoring.