Robust Model Predictive Control for Ship Collision Avoidance Under Multiple Uncertainties
Yingjie Tang, Linying Chen, Junmin Mou, Pengfei Chen, Yamin Huang, Yang Zhou
Abstract
This paper focuses on collision avoidance (CA) for ships under multiple uncertainties. A ship CA framework is designed combining robust motion control of the Own Ship and probabilistic prediction of the Target Ships’ behavior. A motion control method based on the Tube-based Model Predictive Control (MPC) is designed to achieve robust trajectory tracking, considering uncertainties about ship motion and external disturbances. A high-precision probabilistic trajectory prediction method based on GPR with the incremental theory is proposed to describe the uncertain behavior of the TSs. The artificial potential field (APF) method is introduced to deal with the CA constraints in Tube-based MPC, effectively reducing computational complexity. Simulation experiments with different degrees of uncertainty demonstrate the effectiveness of the proposed framework for ship CA.