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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Updated: Jul 13, 2025

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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Event-Triggered Learning-Based Fault Accommodation for a Class of Nonlinear Interconnected Systems.

Dong Zhao, Xiaodong Zhang, Marios M Polycarpou

    IEEE Transactions on Neural Networks and Learning Systems
    |October 17, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a distributed learning fault accommodation scheme for nonlinear systems using event-triggered communication. The method ensures system stability despite faults by using adaptive neural networks for control reconfiguration.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Networked Systems

    Background:

    • Nonlinear interconnected systems are susceptible to process faults.
    • Event-triggered communication offers efficiency but complicates fault management.
    • Existing fault accommodation methods may not handle distributed and interconnected dynamics effectively.

    Purpose of the Study:

    • To propose a distributed learning-based fault accommodation scheme for nonlinear interconnected systems.
    • To address faults in local dynamics and those propagated from neighboring subsystems.
    • To ensure closed-loop stability under event-triggered communication and asynchronous reconfiguration.

    Main Methods:

    • Utilizing an event-triggered nominal control law before fault detection.
    • Implementing an event-triggered fault accommodation law post-fault detection.
    • Employing a neural-network-based adaptive learning scheme for online estimation of fault-tolerant control functions.
    • Analyzing closed-loop stability with asynchronous controller reconfiguration.

    Main Results:

    • The proposed scheme effectively accommodates process faults in nonlinear interconnected systems.
    • Stability analysis confirms system stabilization with derived conditions based on event-triggering parameters.
    • Zeno behavior is successfully excluded, ensuring practical implementation.
    • The scheme was demonstrated on an interconnected inverted pendulum system.

    Conclusions:

    • The developed distributed learning-based fault accommodation scheme enhances the resilience of nonlinear interconnected systems.
    • Event-triggered communication and adaptive neural networks provide an effective framework for fault tolerance.
    • The rigorous stability analysis validates the proposed approach for practical applications.