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    This study presents a fuzzy adaptive event-triggered consensus control for nonlinear multiagent systems (MASs). The proposed method ensures bounded signals and avoids Zeno behavior, even with output constraints and Denial of Service (DoS) attacks.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Systems Engineering

    Background:

    • Nonlinear multiagent systems (MASs) face challenges with output constraints and Denial of Service (DoS) attacks.
    • Achieving consensus control in MASs requires robust strategies to handle uncertainties and external disturbances.

    Purpose of the Study:

    • To develop a fuzzy adaptive event-triggered consensus control for nonlinear MASs.
    • To address output constraints and mitigate the impact of DoS attacks.
    • To ensure stability and bounded signals within the closed-loop system.

    Main Methods:

    • Fuzzy logic systems (FLSs) for approximating unknown nonlinear functions.
    • A novel switching observer to estimate the leader's state and handle DoS attacks.
    • Exponent-dependent barrier Lyapunov functions (BLFs) for output constraint enforcement.
    • Dynamic surface control (DSC) to reduce computational complexity.
    • Event-triggered control mechanisms to optimize system resource usage.

    Main Results:

    • The proposed controller ensures consensus output tracking errors converge to a small neighborhood of zero.
    • All signals within the closed-loop system are proven to be bounded.
    • Zeno behavior is effectively avoided, ensuring practical implementation.
    • Simulation results validate the proposed control strategy's feasibility and effectiveness.

    Conclusions:

    • The developed fuzzy adaptive event-triggered consensus control strategy is effective for nonlinear MASs.
    • The method successfully handles output constraints and DoS attacks while maintaining system stability.
    • The approach offers a robust and computationally efficient solution for complex MASs.