Prescribed Performance Control for Dynamic Positioning Vessels Based on Adaptive Second-Order Fast Nonsingular Terminal Sliding Mode

Abstract

This paper presents the problem of trajectory tracking prescribed performance control for dynamic positioning vessels (DPV) using the combination of second-order fast nonsingular terminal sliding mode control (SOFNTSMC) and adaptive neural networks (ANN). First, a simplified mathematical model of the DPV is built to describe the dynamics. Then, a new-type prescribed performance function is proposed, which can achieve convergence at a specified time and relieve the saturation of control input. In addition, the SOFNTSMC and the ANN are employed to handle the uncertain disturbances and unknown model parameters of the system, which not only solves the chattering phenomenon of control input but also achieves faster convergence rate and tracking accuracy better. Subsequently, with the Lyapunov stability theory, all the signals of the closed-loop system can be achieved to stable in finite time. Finally, numerical simulations are presented to illustrate the effectiveness of the proposed method.