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    This study introduces a resilient learning controller for leader-follower multiagent systems facing cyber-physical attacks and uncertainties. The approach ensures reliable synchronization by using a trust-confidence mechanism within reinforcement learning.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Cybersecurity

    Background:

    • Leader-follower multiagent systems are crucial for coordinated tasks but vulnerable to uncertainties and cyber-physical attacks.
    • Existing control methods often struggle to maintain synchronization under adversarial conditions and unknown system dynamics.

    Purpose of the Study:

    • To develop an autonomous and resilient controller for leader-follower multiagent systems.
    • To address synchronization challenges posed by system uncertainties and cyber-physical attacks.
    • To enhance system robustness through a novel trust-confidence-based reinforcement learning protocol.

    Main Methods:

    • Design of an observer-based distributed H∞ controller to mitigate attack propagation and effects.
    • Derivation of nonhomogeneous game algebraic Riccati equations for H∞ optimal synchronization.
    • Utilization of off-policy reinforcement learning (RL) to solve the derived equations without prior knowledge of system dynamics.
    • Proposal of a trust-confidence-based distributed control protocol to identify and mitigate compromised agents and communication link attacks.

    Main Results:

    • The proposed H∞ controller effectively prevents attack propagation and attenuates their impact on compromised agents.
    • The resilient RL algorithm successfully learns optimal synchronization solutions despite uncertainties and attacks.
    • The trust-confidence mechanism enables agents to identify and disregard data from compromised sources, enhancing overall system reliability.
    • Simulation results validate the effectiveness of the proposed autonomous and resilient control strategy.

    Conclusions:

    • The developed learning-based control protocol provides an autonomous and resilient solution for leader-follower multiagent systems under uncertainty and cyber-physical attacks.
    • The integration of a trust-confidence mechanism significantly improves the system's ability to handle sophisticated attacks and maintain synchronization.
    • This approach offers a robust framework for secure and reliable multiagent coordination in adversarial environments.