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    This study presents a new control strategy for nonlinear multi-agent systems (MASs) using neural networks and dynamic surface control. The method ensures stable tracking and avoids communication waste in complex systems.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Robotics

    Background:

    • Multi-agent systems (MASs) present complex control challenges, especially when dealing with nonlinear and non-strict feedback dynamics.
    • Unknown nonlinear functions in MASs necessitate robust approximation techniques for effective control.
    • Traditional backstepping methods can suffer from 'complexity explosion', hindering practical implementation.

    Purpose of the Study:

    • To develop an asymptotic tracking control strategy for nonlinear, non-strict-feedback multi-agent systems with unknown nonlinearities.
    • To address the 'complexity explosion' issue inherent in backstepping control.
    • To reduce communication resource consumption in MASs via an event-triggered control strategy.

    Main Methods:

    • Utilized radial basis function neural networks (RBF NNs) to approximate unknown nonlinear functions within the MASs.
    • Improved dynamic surface control (DSC) technology to mitigate the 'complexity explosion' problem and eliminate boundary layer influences.
    • Implemented a relative threshold event-triggered strategy to optimize communication, successfully avoiding the Zeno phenomenon.

    Main Results:

    • Ensured all closed-loop system variables are uniformly ultimately bounded (UUB).
    • Achieved zero tracking errors for follower outputs, enabling them to precisely follow the leader's output.
    • Demonstrated the effectiveness of the proposed control scheme through simulation results.

    Conclusions:

    • The combined RBF NNs and improved DSC approach provides a robust solution for asymptotic tracking in nonlinear MASs.
    • The event-triggered strategy effectively conserves communication resources without compromising system stability.
    • The proposed control scheme offers a practical and efficient method for controlling complex multi-agent systems.