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    This study introduces a novel neural network (NN) adaptive controller for nonlinear multiagent systems (MASs) to achieve consensus in fixed-time, overcoming parameter uncertainties and disturbances.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Nonlinear Dynamics

    Background:

    • Nonlinear multiagent systems (MASs) often face challenges with parameter uncertainties and external disturbances.
    • Achieving consensus control in a fixed-time manner is crucial for many real-world applications.
    • Existing control methods may suffer from singularity issues or slow convergence.

    Purpose of the Study:

    • To develop a nonsingular fixed-time adaptive consensus control strategy for nonlinear MASs with parameter uncertainties.
    • To design a novel distributed neural network (NN) adaptive controller that effectively handles disturbances.
    • To ensure fast and stable convergence of tracking errors within a fixed-time interval.

    Main Methods:

    • Introduction of a generalized intermediate-variable-based disturbance observer (IVBDO) for real-time online estimation of mismatched external disturbances.
    • Construction of a distributed fixed-time NN adaptive controller utilizing a quartic Lyapunov function.
    • Development of a control protocol that eliminates singularity phenomena during the fixed-time design process.

    Main Results:

    • The proposed control algorithm guarantees semi-globally uniformly ultimately bounded (SGUUB) system behavior.
    • Distributed output tracking errors are shown to converge to an adjustable compact set around the origin within a fixed-time interval.
    • Simulation results validate the effectiveness and performance of the suggested control approach.

    Conclusions:

    • The novel NN adaptive control strategy effectively addresses nonsingular fixed-time consensus control for nonlinear MASs.
    • The integration of IVBDO and NN-based control provides robust performance against uncertainties and disturbances.
    • The method ensures rapid and stable convergence, demonstrating significant advancements in MAS control theory and practice.