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Distributed Dynamic Event-Triggered Control for Multiagent Systems Under FDI Attack via ESN-Based Adaptive Dynamic

Zhenyu Gong, Feisheng Yang, Chong Liu

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    Summary
    This summary is machine-generated.

    This study introduces a secure consensus control scheme for multiagent systems, effectively countering false data injection attacks using adaptive dynamic programming and an event-triggered mechanism for resource efficiency.

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    Area of Science:

    • Control Systems Engineering
    • Cybersecurity
    • Artificial Intelligence

    Background:

    • Multiagent systems are vulnerable to false data injection (FDI) attacks, compromising their secure operation.
    • Existing control schemes may not adequately address the resource demands of continuous monitoring and control.

    Purpose of the Study:

    • To develop a secure consensus control scheme for multiagent systems resilient to FDI attacks.
    • To enhance network resource efficiency through a dynamic event-triggered mechanism.
    • To ensure closed-loop stability and avoid Zeno behavior in the proposed control system.

    Main Methods:

    • An adaptive sliding mode observer is used for FDI attack estimation and compensation.
    • The secure consensus control problem is reformulated as an optimal control problem.
    • A dynamic event-triggered mechanism is integrated into the optimal control law design.
    • An echo state network, utilizing dual heuristic programming and a single critic neural network, approximates the optimal event-triggered control (ETC) policy.
    • An experience replay mechanism is employed for neural network weight updating, removing persistence of excitation conditions.

    Main Results:

    • The proposed scheme effectively compensates for false data injection attacks in multiagent systems.
    • The dynamic event-triggered mechanism significantly saves network resources.
    • The use of echo state networks and experience replay provides an efficient method for approximating the optimal ETC policy.
    • Closed-loop stability and Zeno behavior avoidance are demonstrated through theoretical analysis and simulations.

    Conclusions:

    • The adaptive dynamic programming-based secure consensus control scheme offers a robust solution for multiagent systems facing FDI attacks.
    • The integration of event-triggered control enhances practical applicability by optimizing network resource utilization.
    • The developed method ensures system stability and reliable performance in adversarial environments.