Trajectory Tracking Control of Unmanned Surface Vessel Formation Based on Fixed-Time Indirect Neural Network Estimator

This paper designs a new control architecture for the formation tracking control problem of Unmanned Surface Vehicles (USVs). Firstly, a second-order maneuvering motion model for USVs is constructed, Secondly, combining the desired trajectory and speed with the state of the USV, a second-order error dynamic system and sliding mode surface are designed. Subsequently, using the state information of the USV and the de-sired trajectory information as inputs, an indirect neural network estimator to estimate the upper bound of the uncertainty experi-enced by the formation is designed. Finally, a fixed-time sliding mode controller is designed to stabilize the formation tracking error. The tracking error of the USV formation converges within a fixed actual time, which improves the speed of formation tracking. The stability of the control scheme is proven using Lyapunov stability theory. The Cybership II model USV is used as the simulation object, and this paper's method is compared with the PID control method and FTC+FDO(fixed-time control and finite-time observer), which validates the advancement of the method proposed in this paper.