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    This study introduces a model-free reinforcement learning (RL) method for optimal consensus control in nonlinear multiagent systems. It effectively handles input constraints and distributed synchronization challenges using actor-critic networks and a gradual transition control strategy.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Robotics

    Background:

    • Optimal consensus control is crucial for coordinating multiagent systems.
    • Nonlinear discrete-time systems with input constraints pose significant control challenges.
    • Solving coupled discrete Hamilton-Jacobi-Bellman (HJB) equations is computationally intensive.

    Purpose of the Study:

    • To develop a model-free reinforcement learning (RL) approach for optimal consensus control in nonlinear discrete-time multiagent systems.
    • To address actuator input constraints and distributed synchronization issues in these systems.

    Main Methods:

    • An actor-critic reinforcement learning framework is employed to solve the optimal consensus control problem.
    • A well-defined cost function guides the online learning updates of actor and critic networks.
    • A gradual transition control (GTC) method, with update-free and update-weak policies, is introduced to manage actuator constraints.
    • A synchronization blocking method is designed to handle distributed synchronization challenges in networked agents.

    Main Results:

    • The proposed RL approach successfully achieves optimal consensus control for nonlinear discrete-time multiagent systems.
    • The gradual transition control method effectively handles actuator limitations.
    • The synchronization blocking method ensures reliable distributed operation.
    • Simulations demonstrate the approach's effectiveness across different scenarios.

    Conclusions:

    • The model-free RL strategy provides an effective solution for optimal consensus control in constrained multiagent systems.
    • The integration of GTC and synchronization blocking enhances the robustness and applicability of the control system.
    • The proposed methods offer a viable framework for real-world distributed control applications.