Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function

TLDR

The study addresses trajectory tracking of a marine surface vessel under output constraints and uncertainties. The paper proposes both full state feedback and output feedback control strategies. The authors design an adaptive neural network controller using an asymmetric barrier Lyapunov function, employing a Moore‑Penrose pseudoinverse for full‑state feedback, a high‑gain observer for output feedback, and validate the approach with numerical simulations. The controller successfully enforces output constraints while ensuring semiglobal uniform boundedness of closed‑loop signals.

Abstract

In this paper, we consider the trajectory tracking of a marine surface vessel in the presence of output constraints and uncertainties. An asymmetric barrier Lyapunov function is employed to cope with the output constraints. To handle the system uncertainties, we apply adaptive neural networks to approximate the unknown model parameters of a vessel. Both full state feedback control and output feedback control are proposed in this paper. The state feedback control law is designed by using the Moore-Penrose pseudoinverse in case that all states are known, and the output feedback control is designed using a high-gain observer. Under the proposed method the controller is able to achieve the constrained output. Meanwhile, the signals of the closed loop system are semiglobally uniformly bounded. Finally, numerical simulations are carried out to verify the feasibility of the proposed controller.