Adaptive Neural Network Control of a Vessel with Output Constraints Using the Asymmetric Barrier Lyapunov Function

In this paper, we consider the trajectory tracking of a marine surface vessel in the presence of constraints and uncertainties. An Asymmetric Barrier Lyapunov Function (ABLF) is employed to deal with the output constraints. Neural networks are used to approximate the system uncertainties, and state feedback control law is designed by using Backstepping techniques and the Moore-penrose inverse in case that all states are known. Under the proposed control, the multiple output constraints are never violated, the signals of the closed loop system are semiglobally uniformly bounded (SGUB), and the asymptotic tracing is achieved. Subsequently, numerical simulations are carried to verify the feasibility of the proposed control law.