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    This study introduces a fault-tolerant adaptive control for multiagent systems using reinforcement learning (RL). It enhances communication efficiency and system stability while addressing sensor faults.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Distributed Systems

    Background:

    • Multiagent systems face challenges in tracking control due to communication constraints and sensor faults.
    • Existing event-triggered control methods may not fully address compensation errors or sensor failures.
    • Reinforcement learning (RL) offers potential for adaptive control but can suffer from local optima.

    Purpose of the Study:

    • To develop a fault-tolerant adaptive multigradient recursive reinforcement learning (RL) event-triggered tracking control scheme.
    • To improve communication resource utilization and system stability in discrete-time multiagent systems.
    • To address sensor faults and reduce online estimation complexity.

    Main Methods:

    • Utilized a multigradient recursive RL algorithm to overcome local optima in gradient descent.
    • Proposed a novel lemma for relative threshold event-triggered control to manage compensation error.
    • Implemented a distributed control method with adaptive compensation to handle sensor faults.
    • Employed Lyapunov stability theorem to prove system stability and boundedness of signals.

    Main Results:

    • The proposed scheme effectively handles sensor faults through adaptive compensation, reducing online parameter estimation.
    • The event-triggered strategy enhances communication efficiency and minimizes negative impacts on tracking accuracy and stability.
    • The multigradient recursive RL algorithm reduces online estimation time due to fewer learning parameters.
    • Stability analysis confirmed that all signals in the closed-loop multiagent systems are semiglobally uniformly ultimately bounded.

    Conclusions:

    • The developed fault-tolerant adaptive control scheme is effective for strict-feedback discrete-time multiagent systems.
    • The integration of RL, event-triggered control, and adaptive compensation offers a robust solution.
    • Simulation examples validated the practical applicability and performance of the proposed control strategy.