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Related Experiment Video

Updated: Dec 6, 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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Decentralized Filtering Adaptive Neural Network Control for Uncertain Switched Interconnected Nonlinear Systems.

Tong Ma

    IEEE Transactions on Neural Networks and Learning Systems
    |October 9, 2020
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    Summary

    This study introduces a new decentralized adaptive neural network control for uncertain nonlinear systems. The framework effectively manages uncertainties and achieves system objectives, outperforming model reference adaptive control.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Nonlinear Dynamics

    Background:

    • Interconnected nonlinear systems often face challenges due to uncertainties and switching dynamics.
    • Decentralized control is crucial for managing complex systems with multiple subsystems.
    • Existing adaptive control methods may struggle with both local and mismatched uncertainties in switched systems.

    Purpose of the Study:

    • To develop a novel decentralized filtering adaptive neural network control framework.
    • To address nonlinear uncertainties and switching phenomena in interconnected systems.
    • To enable robust local and global objective tracking for each subsystem.

    Main Methods:

    • Utilizing Gaussian radial basis function (GRBF) neural networks for uncertainty approximation.
    • Implementing a piecewise constant adaptive law to update control parameters.
    • Deriving a decentralized filtering control law to cancel uncertainties.
    • Applying the average dwell time principle to establish error bounds.

    Main Results:

    • The proposed framework effectively cancels local and mismatched uncertainties.
    • Achieved local objective tracking, which is essential for global objective achievement.
    • Demonstrated superior performance compared to model reference adaptive control (MRAC) in a numerical example.
    • Established performance error bounds for the closed-loop system.

    Conclusions:

    • The decentralized filtering adaptive neural network control is effective for uncertain switched interconnected nonlinear systems.
    • The method provides a robust approach to managing system uncertainties and achieving desired performance.
    • The framework offers a promising alternative to existing adaptive control strategies for complex systems.