Event-Triggered Data-Driven Control for Unmanned Surface Vessel Trajectory Tracking Based on Extended State Observer

This paper proposes an anti-disturbance event-triggered data-driven control strategy for the target tracking control problem of unmanned surface vessel (USV) with external disturbance and uncertainty. First, a dynamic linearization data model considering external disturbance and uncertainty is established for the unknown surge and yaw system model of USV and a smooth anti-disturbance target tracking algorithm is constructed. Based on the data-driven control, an extended state observer is constructed to enhance system robustness by compensating for the disturbance and uncertainty of the system, and an event-triggered mechanism is proposed to alleviate the system bandwidth consumption by reducing unnecessary transmissions. Finally, simulation experiments are conducted on the proposed control method, and the results demonstrate that the proposed control algorithm is feasible when the USV dynamics are completely unknown.