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    This study introduces a novel fuzzy adaptive control for nonlinear multiagent systems (NMASs). The method ensures tracking errors converge to zero in a fixed time, enhancing system stability and performance.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Systems Engineering

    Background:

    • Nonlinear multiagent systems (NMASs) with nonstrict-feedback (NSF) structures present significant control challenges.
    • Existing control methods may suffer from control singularity and lack guaranteed convergence times.
    • Adaptive control strategies are crucial for handling uncertainties in complex systems.

    Purpose of the Study:

    • To develop a fuzzy adaptive fixed-time asymptotic consistent control scheme for NMASs with NSF structures.
    • To design a control method that avoids singularity and ensures fixed-time convergence of tracking errors.
    • To enhance the stability and boundedness of the controlled nonlinear multiagent systems.

    Main Methods:

    • Integration of fuzzy logic systems (FLSs) for their approximation capabilities.
    • Application of fixed-time stability theory to guarantee convergence within a finite, predetermined time.
    • Utilization of plus power integration techniques and Barbalat's Lemma for stability analysis.

    Main Results:

    • A novel fixed-time consistency control method is proposed, effectively avoiding control singularity.
    • The developed fuzzy adaptive control scheme guarantees asymptotic stability of tracking errors.
    • Tracking errors are demonstrated to converge to zero within a fixed time, validated by simulations.

    Conclusions:

    • The proposed fuzzy adaptive fixed-time control scheme is effective for nonlinear multiagent systems with NSF structures.
    • The method ensures robust performance with guaranteed fixed-time convergence and system boundedness.
    • This approach offers a significant advancement in the control of complex multiagent systems.