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Suboptimal Leader-to-Coordination Control for Nonlinear Systems With Switching Topologies: A Learning-Based Method.

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    Summary
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    This study introduces a learning-based method for controlling nonlinear multiagent systems (MASs) with switching topologies. It enables precise leader-to-formation control and optimization, addressing complex challenges in distributed systems.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Robotics

    Background:

    • Cooperative control of multiagent systems (MASs) faces challenges in distributed interaction, nonlinear dynamics, and optimization.
    • Existing methods struggle to simultaneously address these complex, intractable issues in theoretical frameworks.

    Purpose of the Study:

    • To investigate a learning-based approach for leader-to-formation control and optimization in nonlinear MASs.
    • To develop a fully distributed state observer for reconstructing leader dynamics under time-varying switching topologies.
    • To achieve optimal control effects and ensure system stability.

    Main Methods:

    • Designed a fully distributed state observer using neural networks to reconstruct leader signals under jointly connected topology.
    • Transformed formation control into tracking control for subsystems using observer-generated states.
    • Employed integral reinforcement learning with a critic network to approximate the optimal value function, bypassing complex Hamilton-Jacobi-Bellman equations.
    • Utilized an actor network to ensure system stability.

    Main Results:

    • The neural network-based observer accurately reconstructs leader dynamics and state trajectories under switching topologies.
    • The learning-based control strategy effectively achieves leader-to-formation control and optimization for nonlinear MASs.
    • Tracking errors and estimation weighted matrices are proven to be uniformly ultimately bounded, ensuring system stability.

    Conclusions:

    • The proposed learning-based method offers a robust solution for complex control and optimization problems in nonlinear MASs with switching topologies.
    • The integration of neural network observers and reinforcement learning provides a powerful framework for achieving precise control and stability.
    • The method's effectiveness is validated through illustrative examples, demonstrating its practical applicability.