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Optimal Consensus Control Design for Multiagent Systems With Multiple Time Delay Using Adaptive Dynamic Programming.

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    This summary is machine-generated.

    A new data-based adaptive dynamic programming method solves optimal consensus tracking for discrete-time multiagent systems with time delays. This approach ensures system consensus and minimizes costs without needing system dynamics knowledge.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Systems Engineering

    Background:

    • Multiagent systems (MASs) with time delays present significant control challenges.
    • Achieving optimal consensus tracking in such systems is crucial for coordinated behavior.
    • Existing methods often require precise system dynamics, which are not always available.

    Purpose of the Study:

    • To develop a data-driven adaptive dynamic programming (ADP) method for optimal consensus tracking in discrete-time MASs.
    • To address the complexities introduced by multiple time delays within the systems.
    • To enable consensus control without prior knowledge of the system's internal dynamics.

    Main Methods:

    • Utilized causal transformations to establish necessary and sufficient conditions for the equivalent time-delay system.
    • Transformed the optimal tracking problem into a Nash-equilibrium problem within a graphical game framework.
    • Employed an error estimator to derive tracking errors using only input-output data, facilitating a data-based ADP algorithm.
    • Solved coupled Hamilton-Jacobi (HJ) equations to achieve the desired control objectives.

    Main Results:

    • The proposed data-based ADP algorithm effectively minimizes cost functions for MASs.
    • Consensus is achieved in discrete-time MASs despite the presence of multiple time delays.
    • The method successfully operates without requiring explicit knowledge of the system dynamics.
    • A numerical example validated the efficacy and practical applicability of the developed approach.

    Conclusions:

    • The novel data-based ADP method provides an effective solution for optimal consensus tracking in discrete-time MASs with time delays.
    • The approach demonstrates robustness by not requiring system dynamics information, relying solely on input-output data.
    • This work contributes a significant advancement in the field of distributed control for complex multiagent systems.