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    This study introduces a model-free reinforcement learning (RL) approach for multiagent systems (MASs) to achieve leader-follower output synchronization. An event-driven observer and RL controller enable followers to track the leader

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Distributed Systems

    Background:

    • Multiagent systems (MASs) present challenges in coordinating nonidentical agents, especially when information is partially unknown.
    • Traditional control methods often require full system knowledge and high communication overhead.
    • Reinforcement learning (RL) offers a promising avenue for adaptive control in complex systems.

    Purpose of the Study:

    • To develop a model-free reinforcement learning (RL) algorithm for output tracking control in nonidentical linear MASs.
    • To address scenarios where followers have limited or no prior knowledge of the leader's system information.
    • To reduce communication and computational burdens within the MAS.

    Main Methods:

    • An event-driven adaptive distributed observer is proposed to estimate the leader's system matrix and state.
    • An edge-based predictor estimates relative states, and an integral input-based triggering condition manages control input transmission.
    • An RL-based state feedback controller is designed, converting the problem into an optimal control problem solved using an off-policy RL algorithm to learn inhomogeneous algebraic Riccati equations (AREs) online.

    Main Results:

    • The proposed event-driven adaptive observer and RL algorithm enable followers to asymptotically synchronize with the leader's output.
    • The off-policy RL algorithm successfully learns the solution to inhomogeneous AREs without system dynamics knowledge.
    • Numerical simulations validate the theoretical findings, demonstrating the effectiveness of the proposed approach.

    Conclusions:

    • The developed model-free RL strategy with an event-driven observer effectively achieves output tracking and synchronization in nonidentical MASs.
    • The approach significantly reduces communication and computational load, making it practical for real-world applications.
    • This work advances the application of RL in distributed control systems with partial information.