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

• Published in: IEEE transactions on cybernetics Vol. 47; no. 7; pp. 1641 - 1651
• Main Authors: He, Wei, Yin, Zhao, Sun, Changyin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive control; Adaptive systems; Artificial neural networks; barrier Lyapunov function; Closed loops; Computer simulation; constraints; Control theory; Feedback control; Liapunov functions; Lyapunov methods; marine vessel; Mathematical models; Network control; Neural networks; neural networks (NNs); Nonlinear systems; Output feedback; Process controls; State feedback; trajectory tracking; Uncertainty
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2016.2554621

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.