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Distributed Optimal Consensus Control for Multiagent Systems With Input Delay.

Huaipin Zhang, Dong Yue, Wei Zhao

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    This study develops a novel method for distributed optimal consensus control in multiagent systems with input delays. The approach converts delayed systems into delay-free ones, enabling effective control policy learning via adaptive dynamic programming.

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    Area of Science:

    • Control Theory
    • Systems Engineering
    • Artificial Intelligence

    Background:

    • Distributed optimal consensus control is crucial for coordinating multiagent systems.
    • Time-varying input delays pose significant challenges in achieving consensus.
    • Existing methods often struggle with the complexity of heterogeneous systems and delays.

    Purpose of the Study:

    • To develop a robust distributed optimal consensus control strategy for continuous-time heterogeneous linear multiagent systems with time-varying input delays.
    • To transform the complex delayed system into an equivalent delay-free system for simplified analysis and control design.
    • To propose an online learning algorithm for optimal control policy determination.

    Main Methods:

    • Discretization and model transformation to convert continuous-time input-delayed systems into discrete-time delay-free systems.
    • Definition of equivalent performance index functions for both system types.
    • Utilization of Hamilton-Jacobi-Bellman (HJB) equations and a novel value iteration algorithm with a critic-action neural network (NN) for online adaptive dynamic programming.

    Main Results:

    • The optimal consensus control problem of the input-delayed system is effectively transformed into that of the delay-free system.
    • The adaptive dynamic programming algorithm, implemented using a critic-action NN, successfully learns control policies.
    • Uniform ultimate boundedness of consensus errors and weight estimation errors is proven, with convergence of approximated control policies.

    Conclusions:

    • The proposed method effectively addresses distributed optimal consensus control for delayed heterogeneous multiagent systems.
    • The adaptive dynamic programming approach provides a viable solution for online control policy learning in the presence of delays.
    • Simulation examples validate the method's effectiveness and robustness.