Autonomous piloting and berthing based on Long Short Time Memory neural networks and nonlinear model predictive control algorithm

Abstract

Autonomous berthing of large underactuated ships is one of challenge tasks in the development of intelligent and autonomous ships, especially under hybrid disturbances of wind and currents, both trajectory prediction and tracking control are difficult issues due to the complexity of the ship motion. In this paper, a low-speed underactuated ship-handling and motion model which fully consider the disturbances of wind and currents is proposed to support the trajectory prediction and tracking control. In addition, a novel trajectory prediction model base on Long Short Time Memory (LSTM) neural network is presented by exploiting the time series features of the berthing trajectories, in which the proposed accurate ship motion model is used to create teaching data that help improve the data consistent and the accuracy of the trajectory prediction. Furthermore, nonlinear Model Predictive Control (MPC) model based on accurate ship motion model is employed to control the trajectory tracking, which efficiently improves the capability of the anti-disturbance. Experiments were designed to validate the reliability and accuracy of the proposed approach under various disturbances of wind and currents. Experimental results have demonstrated that the proposed approach has outstanding performance and effectiveness under the hybrid disturbances of wind and currents.