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Updated: May 24, 2025

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Observer-Based Human-in-the-Loop Optimal Output Cluster Synchronization Control for Multiagent Systems: A Model-Free

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    Summary
    This summary is machine-generated.

    This study introduces an observer-based human-in-the-loop (HiTL) control for nonlinear multiagent systems (MASs). It achieves optimal output cluster synchronization using reinforcement learning, even with unknown leader inputs.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Robotics

    Background:

    • Multiagent systems (MASs) present complex control challenges, particularly in achieving synchronized behavior.
    • Human-in-the-loop (HiTL) control integrates human operators for enhanced system management.
    • Observer-based control is crucial when system states or inputs are not directly measurable.

    Purpose of the Study:

    • To investigate the observer-based human-in-the-loop (HiTL) optimal output cluster synchronization control for nonlinear MASs.
    • To develop a control strategy that accommodates non-autonomous leaders with unknown, time-varying inputs.
    • To enable follower agents to achieve synchronization with the leader's output without direct access to it.

    Main Methods:

    • Design of a non-autonomous leader with human-monitored input.
    • Development of a prescribed-time convergent observer for the leader's unavailable output.
    • Construction of an augmented system and formulation of a cost function.
    • Application of off-policy reinforcement learning to solve the Hamilton-Jacobian-Bellman equation (HJBE).
    • Implementation using a single critic neural network (NN) trained with the least squares method.

    Main Results:

    • The designed observer achieves practical prescribed-time convergence.
    • The off-policy reinforcement learning algorithm successfully learns the HJBE solution without full system knowledge.
    • The single critic NN approach effectively alleviates computational burden.
    • Simulation results validate the efficacy of the proposed HiTL optimal output cluster synchronization control scheme.

    Conclusions:

    • The developed observer-based HiTL control strategy is effective for nonlinear MASs.
    • Reinforcement learning provides a viable method for solving complex control problems with partial system information.
    • The approach ensures robust and efficient cluster synchronization in multiagent scenarios.