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    This study introduces a finite-time dynamic allocation and control scheme for multiple agents tracking a moving target using k-winners-take-all (k-WTA) and neural networks. Simulations confirm its efficiency, even with fast-moving targets.

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    Area of Science:

    • Robotics
    • Control Systems Engineering
    • Artificial Intelligence

    Background:

    • Cooperative multi-agent systems are crucial for complex tasks like target tracking.
    • Existing methods often lack finite-time convergence or robust disturbance handling.
    • Dynamic allocation of tasks among agents remains a challenge.

    Purpose of the Study:

    • To develop a finite-time dynamic allocation and control scheme for multiple agents tracking a moving target.
    • To ensure finite-time stability and efficient task execution.
    • To address scenarios with high target velocities.

    Main Methods:

    • Utilized k-winners-take-all (k-WTA) for competitive dynamic resource allocation.
    • Implemented a novel finite-time dual neural network with an adaptive-gain activation function for k-WTA realization.
    • Designed a finite-time disturbance compensation control law for capturing or vigilance tasks.
    • Employed Lyapunov analysis to guarantee finite-time system stability.

    Main Results:

    • Successfully demonstrated a finite-time dynamic allocation strategy.
    • The proposed control law enabled agents to perform capturing or return-to-vigilance tasks.
    • Finite-time stability was rigorously proven.
    • Simulations validated the scheme's effectiveness, including challenging high-speed target scenarios.

    Conclusions:

    • The developed scheme provides finite-time convergence for multi-agent target tracking.
    • The novel neural network and control law offer robust and efficient solutions.
    • The approach is effective even when target speeds exceed agent speeds.