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http://dx.doi.org/10.1016/j.ijnaoe.2020.09.004

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

Lee, Daesoo (Division of Naval Architecture and Ocean Systems Engineering, Korea Maritime and Ocean University)
Lee, Seung Jae (Division of Naval Architecture and Ocean Systems Engineering, Korea Maritime and Ocean University)
Publication Information
International Journal of Naval Architecture and Ocean Engineering / v.12, no.1, 2020 , pp. 768-783 More about this Journal
Abstract
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 feedback 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.
Keywords
Dynamic Positioning System (DPS); Motion predictive control; Ship motion prediction; Deep learning; Replay buffer;
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Times Cited By KSCI : 2  (Citation Analysis)
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