Motion predictive control for DPS using predicted drifted ship position based on deep learning and replay buffer

• Main Authors: Daesoo Lee, Seung Jae Lee
• Format: Article
• Language: English
• Published: Elsevier 2020-01-01
• Series: International Journal of Naval Architecture and Ocean Engineering
• Subjects: Dynamic Positioning System (DPS); Motion predictive control; Ship motion prediction; Deep learning; Replay buffer
• Online Access: http://www.sciencedirect.com/science/article/pii/S2092678220300418

Typically, a Dynamic Positioning System (DPS) uses a PID feed-back system, and it often adopts a wind feed-forward system because of its easier implementation than a feed-forward system based on current or wave. But, because a ship’s drifting motion is caused by wind, current, and wave drift loads, all three environmental loads should be considered. In this study, a motion predictive control for the PID feed-back system of the DPS is proposed, which considers the three environmental loads by utilizing predicted drifted ship positions in the future since it contains information about the three environmental loads from the moment to the future. The prediction accuracy for the future drifted ship position is ensured by adopting deep learning algorithms and a replay buffer. Finally, it is shown that the proposed motion predictive system results in better station-keeping performance than the wind feed-forward system.