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    This study introduces a novel finite-time model-free adaptive consensus control (FMFACC) for nonlinear multiagent systems (MASs). The FMFACC achieves rapid consensus tracking in finite time, overcoming limitations of previous asymptotic methods.

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    Area of Science:

    • Control Theory
    • Systems Engineering
    • Robotics

    Background:

    • Existing model-free adaptive consensus control (MFACC) strategies for nonlinear multiagent systems (MASs) typically achieve only asymptotic tracking performance.
    • There is a need for control strategies that offer faster convergence and improved robustness in nonlinear MASs with unknown dynamics.

    Purpose of the Study:

    • To propose a novel finite-time MFACC (FMFACC) approach for nonlinear MASs with completely unknown dynamics.
    • To achieve rapid consensus tracking performance beyond asymptotic limits.
    • To unify and extend prior theoretical frameworks in adaptive consensus control.

    Main Methods:

    • Development of a new finite-time consensus tracking error incorporating a variable proportional coefficient and an adjacent maximum tracking error factor.
    • Establishment of time-varying data models to capture unknown nonlinear dynamics.
    • Design of a distributed finite-time consensus tracking control law with a data-driven adaptive gain matrix.
    • Application of an equivalent system transformation strategy for rigorous analysis.

    Main Results:

    • The proposed FMFACC ensures finite-time convergence of the consensus tracking error, accounting for output couplings.
    • A data-driven adaptive gain matrix enables model-free fast coordination of agents along a predefined trajectory.
    • Rigorous analysis proves asymptotically finite-time consensus tracking despite unknown system dynamics and output couplings.
    • Simulation and experimental studies validate the superior performance of the FMFACC approach.

    Conclusions:

    • The novel FMFACC approach effectively achieves rapid and robust consensus tracking in nonlinear MASs with unknown dynamics.
    • This method overcomes the asymptotic performance limitations of existing MFACC strategies.
    • The FMFACC provides a unified and advanced framework for nonlinear MAS control.