Robust nonlinear model predictive control for ship dynamic positioning using Laguerre function

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Hou, Xiuhui, Deng, Fang, Yang, Hualin, Yu, Dunjing, Zhang, Hanlin, Li, Boyang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Attitude control; Constraints; Disturbances; input constraints; Kalman filters; Laguerre functions; Linear systems; Marine vehicles; Nonlinear control; Nonlinear dynamical systems; nonlinear model predictive control; Optimization; Predictive control; Predictive models; Robust control; ship dynamic positioning; Trajectory
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3222762

This paper is devoted to the issue of computationally efficient and robust nonlinear model predictive control (NMPC) for ship dynamic positioning (DP) systems subjected to input constraints and unknown environmental disturbances. The Laguerre functions, typically applied to the linear systems, are introduced to the constrained NMPC design of the nonlinear DP system to reduce the computational burden. The unscented Kalman filter is adopted to estimate the unknown disturbances and states; thus, the disturbance estimates are utilized as the cancellation signal to achieve robust offset-free control. Simulations of the proposed Laguerre function-based NMPC scheme are implemented and compared with the performance of typical Laguerre function-based linear model predictive control (LMPC) for the DP system. Simulation results well demonstrate the effectiveness, robustness and superiority of the proposed controller.