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Fuzzy Adaptive Event-Triggered Consensus Control for Nonlinear Multiagent Systems Under Jointly Connected Switching

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    Summary
    This summary is machine-generated.

    This study introduces a fuzzy adaptive event-triggered (ET) consensus control for nonlinear multiagent systems (NMASs). The novel approach optimizes network use by enabling intermittent communication, ensuring system stability and accurate tracking.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Networked Systems

    Background:

    • Nonlinear multiagent systems (NMASs) face challenges in achieving consensus under jointly connected switching networks, especially when leader information is unknown.
    • Continuous data transmission in NMASs leads to inefficient network channel utilization.
    • Existing control strategies may not adequately address the complexities of unknown dynamics and intermittent communication.

    Purpose of the Study:

    • To develop a fuzzy adaptive event-triggered (ET) consensus control strategy for NMASs operating under jointly connected switching networks.
    • To address the challenge of unknown leader states and dynamics by proposing a novel distributed ET reference generator.
    • To enhance network channel utilization by minimizing continuous information transmission among agents.

    Main Methods:

    • A novel distributed event-triggered (ET) reference generator is designed to estimate unknown leader states and their derivatives.
    • Fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear dynamics within the NMASs.
    • A backstepping control methodology is employed to design the fuzzy adaptive ET consensus control algorithm, facilitating intermittent communication.

    Main Results:

    • The proposed fuzzy adaptive ET consensus control algorithm ensures that all closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB).
    • Tracking errors within the NMASs are demonstrated to converge to a small neighborhood around zero.
    • The developed algorithm effectively manages intermittent communication, optimizing network resource usage.

    Conclusions:

    • The fuzzy adaptive ET consensus control algorithm is effective for nonlinear multiagent systems under jointly connected switching networks.
    • The approach successfully handles unknown system dynamics and leader information through fuzzy approximation and an ET reference generator.
    • Simulations involving unmanned surface vehicles (USVs) confirm the practical applicability and effectiveness of the proposed control strategy.