Ship Control of Approach Maneuvering Under Wind Disturbance Using a Deep Neural Network

The effect of wind disturbance is one of the major factors that complicate heading control in ship control at low speeds. Approach maneuvering while decreasing speed and approaching the pier requires a control adopted to changing maneuverability and wind conditions. In this study, a control scheme using a deep neural network (DNN) was developed to output command values corresponding to wind conditions by training the DNN on maneuvering patterns of approach maneuvering under various wind conditions. The developed control scheme was validated through experiments using an actual ship.