Robust Path Tracking and Obstacle Avoidance using Tube-based Model Predictive Control for Surface Vehicles

Abstract

This paper describes a tube-based model predictive control (TMPC) approach to robust path tracking and obstacle avoidance for surface vehicles. The TMPC algorithm consists of a nominal model predictive control and a state feedback control, which ensure that the state and input constraints are always satisfied under uncertain environmental disturbances. For obstacle avoidance, a robust positively invariant (RPI) set that contains the vehicle's position must be effectively calculated. To this end, the vehicle's dynamics are decoupled into surge and sway-yaw subsystems based on the vehicle's error dynamics with regard to the desired path, and zonotopes are used for the RPI set calculation. A TMPC is then designed using the obtained RPI set for each subsystem. Simulation results are presented to verify the effectiveness of the proposed control algorithm.