Machine learning prediction of 6-DOF motions of KVLCC2 ship based on RC model

Abstract

This study uses a machine learning technique based on the Reservoir Computing (RC) model to predict the surge, sway, heave, roll, pitch, and yaw (6-DOF) motions of the KVLCC2 ship in an irregular wave environment. The trained RC model can predict the 6-DOF motions and give the predicted length of 2–5 wave cycles ahead with good accuracy. This work shows the strong ability of machine learning to predict vessel wave-excited motions. It implies that machine learning has important guiding significance in real-time forecasting for motions of both manned and unmanned ships.