Hongying Cheng

This article proposes an adaptive sliding mode fault-tolerant tracking control scheme for underactuated unmanned surface vehicles (USVs) that suffer from loss of effectiveness and increase in bias input when performing path tracking. First, the mathematical model and fault model of USVs are introduced. Then, the USV is driven along the planned path by back-stepping and fast terminal sliding mode control. The radial basis function (RBF) neural network is used to approximate the unknown external disturbances caused by wind, waves, and currents, the unmodeled dynamics of the system, the actuator non-executed portions and bias faults. An adaptive law is designed to account for the loss of effectiveness of the thruster. In addition, through the analysis of Lyapunov stability criteria, it is proved that the proposed control method can asymptotically converge the tracking error to zero. Finally, this paper uses a simulation to demonstrate that, when a fault occurs, the tracking effect of the fault-tolerant control method proposed in this paper is almost the same as that without a fault, which proves the effectiveness of the designed adaptive law.