Dynamic obstacle avoidance of unmanned ship based on event-triggered adaptive nonlinear model predictive control

Abstract

This article studies trajectory tracking and dynamic obstacle avoidance problems of unmanned ships, and an event-triggered adaptive nonlinear model predictive control (EANMPC) method is constructed to solve the above problems. Firstly, a novel adaptive nonlinear model predictive control is used to track the trajectory of the unmanned ship. And then, an obstacle avoidance mechanism is formulated to deal with the condition of dynamic obstacles that appear during the unmanned ship trajectory tracking process. Secondly, an event-triggered mechanism is established to reduce the computational burden of the controller. Finally, several simulation results are provided for demonstrating the effectiveness and advantage of EANMPC.