Path following of autonomous surface vehicles with line-of-sight and nonlinear model predictive control
Zhuoer Tian, Huarong Zheng, Wen Xu
Abstract
This paper proposes a novel quasi-infinite nonlinear model predictive control (NMPC) method for the path following of autonomous surface vehicles (ASVs). Firstly, the nonlinear ASV dynamics are converted into path following error dynamics characterizing the relationship between the ASV and the predefined reference path. In this way, the original tracking control problem is transformed into a stabilization problem and the problem size is reduced due to fewer variables. The proposed quasi-infinite NMPC is then applied to ensure that the error states converge to the origin with designed closed-loop system stability. Moreover, within the framework of Line-of-sight (LOS) guidance, the convergence of the error dynamics to the origin guarantees that the convergence of the ASV to the reference path. In addition, we improve the terminal constraints and terminal costs in the conventional quasi-infinite NMPC scheme so that a larger region of attraction is achieved. Simulation results demonstrate the effectiveness and certain degree of robustness of the proposed NMPC based ASV path following approach.