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    This study introduces a fixed-time distributed average tracking control for nonlinear multiagent systems. The proposed framework ensures all agents converge to the average of reference signals within a fixed time, even with disturbances.

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    Area of Science:

    • Control Systems Engineering
    • Nonlinear Systems Theory
    • Distributed Systems

    Background:

    • Multiagent systems often face challenges in achieving coordinated behavior, especially under external disturbances.
    • Distributed average tracking (DAT) is crucial for consensus and collaborative tasks in networked systems.
    • Achieving fixed-time convergence, where convergence occurs within a predetermined finite time, is highly desirable for practical applications.

    Purpose of the Study:

    • To investigate and solve the fixed-time distributed average tracking (DAT) problem for nonlinear multiagent systems with unity relative degree.
    • To develop a robust distributed control framework capable of handling external disturbances.
    • To ensure fixed-time convergence of all agents' outputs to the average of nonlinear reference signals.

    Main Methods:

    • Development of a distributed control framework.
    • Introduction of a steady-state generator to reconstruct the desired average trajectory.
    • Design of a distributed observer for robust target trajectory estimation, mitigating initialization errors.
    • Implementation of an observer-based output-feedback controller for fixed-time convergence.

    Main Results:

    • The proposed control architecture guarantees fixed-time convergence of all agents' outputs to the target trajectory.
    • The system demonstrates robustness against external disturbances.
    • Theoretical analysis confirms the effectiveness of the fixed-time DAT solution.
    • Numerical simulations validate the proposed method's performance.

    Conclusions:

    • The developed distributed control framework successfully addresses the fixed-time DAT problem for nonlinear multiagent systems.
    • The approach ensures rapid and reliable convergence within a finite, predetermined time.
    • The method is effective even in the presence of external disturbances and initialization uncertainties.