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Data-Based Optimal Synchronization Control for Discrete-Time Nonlinear Heterogeneous Multiagent Systems.

Hao Fu, Xin Chen, Wei Wang

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    This study presents a data-driven optimal synchronization control strategy for unknown nonlinear multiagent systems (MASs). It overcomes challenges in solving complex equations using a novel hierarchical and distributed framework.

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    Area of Science:

    • Control Engineering
    • Systems Science
    • Applied Mathematics

    Background:

    • Optimal synchronization in unknown discrete-time nonlinear heterogeneous multiagent systems (MASs) is challenging due to intractable coupled Bellman's equations.
    • Existing methods struggle with analytical solutions for these complex systems.

    Purpose of the Study:

    • To develop a data-based optimal synchronization control strategy for unknown discrete-time nonlinear heterogeneous MASs.
    • To address the intractability of coupled Bellman's equations in achieving optimal synchronization.

    Main Methods:

    • A hierarchical and distributed optimal control framework is proposed, integrating a model reference adaptive control (MRAC) layer and a distributed control layer.
    • An offline MRAC algorithm is used for subsystems to track reference models.
    • A distributed reference policy iteration algorithm solves coupled composite nonlinear Bellman's equations for synchronization.

    Main Results:

    • The proposed strategy transforms the problem into a homogeneous MAS synchronization task.
    • Optimal synchronization is achieved by solving the derived Bellman's equations.
    • Suboptimal control is attained through further optimization, with rigorous convergence analysis provided.

    Conclusions:

    • The data-based optimal synchronization control strategy effectively addresses challenges in nonlinear heterogeneous MASs.
    • Simulation results confirm the strategy's effectiveness and the convergence of the proposed algorithms.