The unmanned surface vessel (USV) plays an important role in smart ocean. This study proposes an adaptive fault-tolerant tracking control for USVs in the presence of input saturations and error constraints. A tan-type barrier Lyapunov function is utilised for the error constraints and the neural networks are employed to treat the model uncertainty. Moreover, the adaptive technique combined with the backstepping method not only enables the actuator fault-tolerant controller to address the fault effects but also handles the external disturbances and input saturations. The proposed control approach can track the desired trajectory with error constraints and the system is guaranteed to be uniformly bounded under certain actuator failure. Numerical simulation is carried out to verify the effectiveness of this control strategy.

中文翻译：

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具有输入饱和和误差约束的基于自适应神经网络的无人水面舰艇容错轨迹跟踪控制

无人水面舰艇（USV）在智慧海洋中起着重要作用。这项研究提出了在存在输入饱和和误差约束的情况下对USV的自适应容错跟踪控制。tan型势垒Lyapunov函数用于误差约束，而神经网络用于处理模型不确定性。此外，自适应技术与后推法相结合不仅使执行器容错控制器能够解决故障影响，而且还可以处理外部干扰和输入饱和。所提出的控制方法可以在误差约束下跟踪期望的轨迹，并且保证系统在某些执行器故障下均匀地受约束。进行了数值模拟，以验证该控制策略的有效性。