Adaptive neural finite-time formation control for multiple underactuated vessels with actuator faults

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.