System Identification of Ships Using Machine Learning and Database

Abstract

Ships are more difficult to automate than other vehicles. This is because they are exposed to disturbances such as tidal currents, waves, winds, etc., and their dynamic characteristics fluctuate greatly during the voyage. To date, autopilots have become popular for assisting voyages. However, there are various issues in its complete automation. For example, in a questionnaire regarding the disadvantages, there are answers that "they are susceptible to the tide" or "the displacement due to disturbance cannot be corrected". In this study, an online system identification method is designed. Specifically, the system parameters of the hull motion model are updated only when the error of the turning angular velocity between the ship and the estimated hull motion model exceeds a reference value. At that time, after a rough local linear approximation model is designed using a database, the finish is adjusted using machine learning by particle swarm optimization. Finally, the effectiveness of the method is quantitatively verified by simulation using the input and output data of the ship.