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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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    A new convergent estimation mechanism (CEM) estimates states and faults in nonlinear fuzzy systems. This method proves error convergence, improving upon existing bounded-error techniques for time-varying faults and disturbances.

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    Area of Science:

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

    Background:

    • Nonlinear Takagi-Sugeno fuzzy systems often face challenges with time-varying process faults and input disturbances.
    • Existing estimation methods struggle to prove convergence for time-varying faults, often only achieving uniformly ultimately bounded errors.

    Purpose of the Study:

    • To develop a novel convergent estimation mechanism (CEM) for nonlinear Takagi-Sugeno fuzzy systems with both time-varying faults and input disturbances.
    • To prove the convergence of estimation errors for both system states and faults to zero.

    Main Methods:

    • Construction of a convergent estimation mechanism (CEM) using a set of fuzzy iterative estimation observers.
    • Application of a suitable isolation technique to effectively separate system disturbances within the fuzzy iterative error dynamics.

    Main Results:

    • The proposed CEM demonstrates the convergence of the mean sequence of estimation errors (for states and faults) to zero.
    • The method effectively isolates input disturbances in the error dynamics, a key improvement over prior work.
    • Numerical simulations validate the effectiveness and advantages of the developed CEM.

    Conclusions:

    • The developed CEM provides a robust approach for state and fault estimation in complex nonlinear fuzzy systems.
    • This work advances fault estimation by proving error convergence, surpassing previous uniformly ultimately bounded results for time-varying faults.