This study investigates the course-tracking problem for the unmanned surface vehicle in the presence of constraints of the actuator faults, control gain uncertainties, and environmental disturbance. A novel event-triggered robust neural control algorithm is proposed by fusing the robust neural damping technique and the event-triggered input mechanism. In the algorithm, no prior information of the system model about the unknown yawing dynamic parameters and unknown external disturbances is required. The transmission burden between the controller and the actuator could be relieved. Moreover, the control gain-related uncertainties and the unknown actuator faults are compensated through two updated online adaptive parameters. Sufficient effort has been made to verify the semi-global uniform ultimate bounded stability for the closed-loop system based on Lyapunov stability theory. Finally, simulation results are presented to illustrate the effectiveness and superiority of the proposed algorithm.

这项研究调查了在执行器故障，控制增益不确定性和环境干扰的约束下，无人水面车辆的航迹跟踪问题。通过结合鲁棒神经阻尼技术和事件触发输入机制，提出了一种新颖的事件触发鲁棒神经控制算法。在该算法中，不需要关于未知偏航动态参数和未知外部干扰的系统模型的先验信息。可以减轻控制器和执行器之间的传输负担。此外，通过两个更新的在线自适应参数来补偿与控制增益有关的不确定性和未知的执行器故障。基于李雅普诺夫稳定性理论，已经做出了足够的努力来验证闭环系统的半全局一致极限极限稳定性。最后，仿真结果表明了该算法的有效性和优越性。