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    This study introduces compound tracking control for vehicular platoons, achieving finite-time stability and convergence. The new finite-time performance function ensures faster tracking, independent of initial conditions.

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    Area of Science:

    • Control Systems Engineering
    • Robotics
    • Artificial Intelligence

    Background:

    • Vehicular platoons require advanced control for cooperative tasks.
    • Existing methods face limitations in achieving simultaneous finite-time stability and asymptotic convergence.

    Purpose of the Study:

    • To propose a novel compound tracking control definition for vehicular platoons.
    • To develop a distributed cooperative regulation protocol enhancing tracking performance.

    Main Methods:

    • Introduced a "compound tracking control" definition.
    • Designed a "finite-time performance function" for accelerated convergence.
    • Employed adaptive neural networks (NN), prescribed performance, and backstepping techniques.

    Main Results:

    • Achieved practical finite-time stability and asymptotic convergence simultaneously.
    • Demonstrated a faster convergence rate compared to existing methods.
    • Developed a distributed cooperative protocol with convergence time independent of initial values and parameters.

    Conclusions:

    • The proposed compound tracking control and finite-time performance function effectively address vehicular platoon coordination.
    • The adaptive NN-based distributed protocol ensures robust and efficient cooperative tracking.