This paper proposes a novel neural finite-time formation control algorithm for multiple underactuated surface vessels with actuator faults. In the algorithm, the leader-follower formation problem is formulated as a two-stage tracking problem. First, to address the leader-follower configuration without the information of leader velocity, the virtual vessel is designed to track the reference trajectory of the leader. Second, an adaptive finite-time fault-tolerant control (AFFTC) algorithm is presented for the follower to track the virtual vessel. By fusion of the neural network (NN) and the fractional power, the model uncertainty is approximated and the finite-time convergence is obtained. Furthermore, a concise adaptive law is developed to compensate the upper bounded of NN weights and lump disturbance which include the approximation error of NN, control gain uncertainty, actuator faults and marine environmental disturbance. On the basis of Lyapunov theory, stability analysis proves that all the signals in the closed-loop system are practical finite-time stable. Finally, numerical simulations are performed to demonstrate the performance and superiority of the proposed algorithm.

针对具有执行器故障的多个欠驱动水面舰船，提出了一种新颖的神经网络有限时形成控制算法。在该算法中，前导跟随者形成问题被表述为两阶段跟踪问题。首先，为了在没有引导者速度信息的情况下解决引导者跟随者的配置，虚拟船只被设计为跟踪引导者的参考轨迹。其次，提出了一种自适应的有限时间容错控制（AFFTC）算法，用于跟随者跟踪虚拟船。通过融合神经网络和分数次幂，模型不确定性得以近似，并获得了有限时间收敛性。此外，还开发了一种简洁的自适应律来补偿NN权重和团块干扰的上限，其中包括NN的近似误差，控制增益不确定性，执行器故障和海洋环境干扰。根据李雅普诺夫理论，稳定性分析证明闭环系统中的所有信号都是实际的有限时间稳定。最后，进行了数值模拟，以证明该算法的性能和优越性。