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    This study introduces a novel control strategy for nonlinear multiagent systems, effectively managing time-varying delays. The proposed method ensures accurate tracking by using neural networks (NN) and an event-triggered mechanism, reducing communication updates.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Systems Engineering

    Background:

    • Nonlinear multiagent systems often face challenges with state estimation and time-varying input delays.
    • Existing control strategies may struggle to maintain tracking accuracy under such complex conditions.

    Purpose of the Study:

    • To develop a dynamic event-triggered adaptive output-feedback tracking control strategy.
    • To address the challenges of immeasurable states and time-varying input delays in nonlinear multiagent systems.

    Main Methods:

    • Utilized neural networks (NN) for state estimation via a low-gain nonlinear observer.
    • Designed an auxiliary system for time-varying delay compensation.
    • Proposed a distributed adaptive composite NN dynamic surface control (DSC) strategy.
    • Implemented a switching dynamic event-triggered mechanism to optimize controller updates.

    Main Results:

    • Achieved satisfactory tracking accuracy in nonlinear multiagent systems.
    • Successfully compensated for time-varying input delays.
    • Demonstrated convergence of consensus tracking error to a residual set.
    • Validated the effectiveness of the proposed composite NN DSC scheme through simulations.

    Conclusions:

    • The proposed control strategy effectively handles nonlinear multiagent systems with immeasurable states and time-varying input delays.
    • The integration of NN, DSC, and event-triggering significantly enhances tracking performance and communication efficiency.
    • Simulation results confirm the robustness and efficacy of the developed control scheme.