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Distributed Optimal Consensus Control for Nonlinear Multiagent System With Unknown Dynamic.

Jilie Zhang, Huaguang Zhang, Tao Feng

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    Summary
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    This study introduces adaptive dynamic programming (ADP) for optimal cooperative control in nonlinear multiagent systems (MASs) with unknown dynamics. The novel method ensures consensus protocols converge to optimal solutions without needing system knowledge.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Robotics

    Background:

    • Multiagent systems (MASs) present challenges in distributed control, especially with unknown dynamics.
    • Achieving optimal cooperative control and consensus in such systems is a significant research problem.

    Purpose of the Study:

    • To develop a novel adaptive dynamic programming (ADP) approach for distributed optimal cooperative control in continuous-time nonlinear MASs with unknown dynamics.
    • To circumvent the need for prior knowledge of system dynamics in ADP-based control design.

    Main Methods:

    • Introduction of predesigned extra compensators to derive augmented neighborhood error systems.
    • Application of a policy iteration algorithm, theoretically proving convergence to the Hamilton-Jacobi-Bellman equation solution.
    • Development of an online iterative scheme using sampled data and value function gradients.

    Main Results:

    • The proposed method successfully bypasses the requirement for system dynamics knowledge in ADP.
    • Optimal consensus protocols are shown to be solutions to the multiagent system differential game.
    • Neural networks are utilized for implementation, with weights updated to approximate optimal consensus protocols.

    Conclusions:

    • The novel online iterative scheme effectively achieves distributed optimal cooperative control for nonlinear MASs with unknown dynamics.
    • The theoretical framework guarantees convergence of the value function sequence.
    • Numerical examples validate the proposed scheme's effectiveness.