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Related Experiment Video

Updated: Sep 20, 2025

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Adaptive Neural Network-Based Asynchronous Control for Switching Cyber-Physical Systems With Unknown Dead Zone.

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    Summary
    This summary is machine-generated.

    This study introduces adaptive neural network control for switching cyber-physical systems with unknown dead zones. The method ensures system stability despite asynchronous switching and unknown inputs, validated by simulations.

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    Area of Science:

    • Control Systems Engineering
    • Cyber-Physical Systems
    • Artificial Intelligence

    Background:

    • Switching cyber-physical systems (CPS) present control challenges due to asynchronous switching and unknown system parameters.
    • Existing methods often rely on Markov/semi-Markov processes, which can be computationally intensive.
    • Unknown dead zones in network environments further complicate control design for CPS.

    Purpose of the Study:

    • To develop an adaptive neural network control strategy for switching CPS with unknown dead zones.
    • To analyze system behavior using a generalized switching rule, reducing computational load.
    • To ensure the stability and boundedness of the closed-loop system under uncertain conditions.

    Main Methods:

    • Utilized a generalized switching rule to model subsystem switching dynamics.
    • Employed adaptive neural networks for control law design to handle unknown dead zones.
    • Developed a dynamically adjusted saturation-based observer to mitigate unforeseen information effects.
    • Applied a Lyapunov function approach for stability analysis, ensuring boundedness in probability.

    Main Results:

    • Established sufficient criteria for ensuring the closed-loop system remains bounded in probability.
    • Demonstrated the effectiveness of the adaptive neural network control law in managing unknown dead zones.
    • Showcased the flexibility of the saturation-based observer through dynamic adjustment of its saturation level.

    Conclusions:

    • The proposed adaptive neural network asynchronous control methodology is effective for switching CPS with unknown dead zones.
    • The generalized switching rule and observer design offer practical advantages in terms of computational load and flexibility.
    • Simulation results validate the robustness and practicality of the developed control strategy.