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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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Fault Estimation for Fuzzy Delay Systems: A Minimum Norm Least Squares Solution Approach.

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    This study introduces a minimum norm least squares solution (MNLSS) for fault estimation in Takagi-Sugeno fuzzy systems. The MNLSS approach enhances fault estimation accuracy by minimizing state error effects.

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    Area of Science:

    • Control Systems Engineering
    • Fuzzy Logic Systems
    • Fault Diagnosis

    Background:

    • Takagi-Sugeno fuzzy systems are widely used in control applications.
    • State delays can significantly impact system performance and stability.
    • Accurate fault estimation is crucial for reliable system operation.

    Purpose of the Study:

    • To develop an effective fault estimation method for Takagi-Sugeno fuzzy systems with state delays.
    • To introduce a novel fault estimation compensator using the minimum norm least squares solution (MNLSS) approach.
    • To demonstrate the superiority of the proposed method over existing techniques.

    Main Methods:

    • A minimum norm least squares solution (MNLSS) approach is employed.
    • A fault estimation compensator is designed to optimize the fault estimator.
    • The method is validated using three illustrative examples.

    Main Results:

    • The proposed MNLSS-based fault estimation method effectively reduces the impact of state errors.
    • The fault estimator's accuracy is significantly improved compared to conventional methods.
    • The effectiveness and advantages of the MNLSS approach are demonstrated through simulations.

    Conclusions:

    • The MNLSS approach provides an effective solution for fault estimation in delayed Takagi-Sugeno fuzzy systems.
    • This method offers enhanced accuracy and robustness against state errors.
    • The proposed technique is a valuable contribution to fault diagnosis in complex systems.