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    This study introduces a novel data-driven distributed control method for leader-follower multiagent systems. It uses reinforcement learning to achieve cooperative optimal output regulation without needing system dynamics, enhancing control adaptability.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Robotics

    Background:

    • Cooperative output regulation is crucial for multiagent systems.
    • Traditional methods often require knowledge of system dynamics.
    • Leader-follower architectures present unique control challenges.

    Purpose of the Study:

    • To propose a data-driven distributed control method for cooperative optimal output regulation in leader-follower multiagent systems.
    • To develop a distributed adaptive internal model for estimating leader dynamics.
    • To enable optimal controller learning using reinforcement learning without prior system knowledge.

    Main Methods:

    • A distributed adaptive internal model comprising a distributed internal model and observer was developed.
    • Two reinforcement learning algorithms, policy iteration and value iteration, were employed.
    • Online input and state data were used to learn the optimal controller and estimate leader states.

    Main Results:

    • The proposed method effectively solves the cooperative optimal output regulation problem.
    • The distributed adaptive internal model successfully estimates the leader's dynamics.
    • Reinforcement learning algorithms learned optimal controllers from online data.

    Conclusions:

    • This work establishes a foundation for integrating data-driven distributed control with adaptive dynamic programming.
    • The developed method offers a robust approach to cooperative control in multiagent systems.
    • The approach enhances adaptability and reduces reliance on predefined system models.