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Event-Based Robust Optimal Consensus Control for Nonlinear Multiagent System With Local Adaptive Dynamic Programming.

Jie Wang, Zitao Zhang, Bailing Tian

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    Summary
    This summary is machine-generated.

    This study presents a robust optimal consensus method for nonlinear multiagent systems (MASs) using adaptive dynamic programming (ADP) and event-triggered control. The approach ensures system stability and convergence despite disturbances and nonlinearities.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Robotics

    Background:

    • Nonlinear multiagent systems (MASs) face challenges in achieving robust consensus due to inherent nonlinearities and external disturbances.
    • Existing control methods often require full state information or continuous communication, limiting practical applications.

    Purpose of the Study:

    • To develop a robust optimal consensus strategy for nonlinear MASs.
    • To address unmatched disturbances and measurement errors using adaptive dynamic programming (ADP) and event-triggered control.
    • To relax the requirement of initial admissible control for improved practicality.

    Main Methods:

    • A novel measurement error is defined to construct a distributed integral sliding-mode controller for fixed-time convergence.
    • A modified cost function with augmented control is proposed to handle unmatched disturbances in an event-based optimal consensus controller.
    • A single-network local ADP structure with concurrent learning is employed to approximate optimal consensus policies.

    Main Results:

    • Consensus errors approximately converge to the origin in a fixed time.
    • The proposed method guarantees the robustness of MASs.
    • Uniform ultimate boundedness (UUB) of neural network (NN) weights' estimation error is achieved, ensuring stability.

    Conclusions:

    • The developed method effectively achieves robust optimal consensus in nonlinear MASs.
    • The combination of ADP and event-triggered control offers a practical and stable solution.
    • Simulation results validate the proposed approach's effectiveness.