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    Area of Science:

    • Robotics
    • Control Systems Engineering
    • Artificial Intelligence

    Background:

    • Unmanned Surface Vehicles (USVs) require advanced control for precise trajectory tracking.
    • Actuator wear is a significant challenge impacting the longevity and performance of USVs.
    • Existing control strategies often do not adequately address actuator wear in optimal control problems.

    Purpose of the Study:

    • To develop an optimal control algorithm for USVs that minimizes actuator wear while improving trajectory tracking.
    • To investigate the USV control system by dividing it into kinematic and kinetic subsystems.
    • To enhance control performance by approximating nonlinear uncertainties and compensating for environmental disturbances.

    Main Methods:

    • The USV control system is decomposed into kinematic and kinetic subsystems with defined performance indexes.
    • Actor-critic neural networks are employed to approximate value functions, Hamilton-Jacobi-Bellman equations, and optimal control policies.
    • An event-triggered zero-sum game approach using a minmax strategy is implemented to reduce propeller and rudder wear.

    Main Results:

    • The proposed algorithm effectively approximates USV nonlinear uncertainties and compensates for environmental disturbances.
    • The closed-loop control system demonstrated semi-globally uniformly ultimately bounded stability, verified by Lyapunov theory.
    • Simulations and harbor experiments confirmed the algorithm's superior performance and engineering applicability.

    Conclusions:

    • The developed optimal control strategy successfully addresses actuator wear in USVs.
    • The integration of actor-critic networks and zero-sum game theory offers a robust solution for USV trajectory tracking.
    • The algorithm presents a promising approach for enhancing the operational efficiency and lifespan of unmanned surface vehicles.