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While automatic controllers are frequently used during transit operations and low-speed maneuvering of ships, ship operators typically perform docking maneuvers. This task is more or less challenging depending on factors such as local environment disturbances, number of nearby vessels, and the speed of the ship as it docks. This paper proposes a tool for onboard support that offers position predictions based on an integration of a supervised machine learning (ML) model of the ship into the ship dynamic model. The ML model is applied as a compensator of the unmodelled behaviour or inaccuracies from the dynamic model. The dynamic model increases the amount of predetermined knowledge about how the vessel is likely to move and thus reduces the black-box factor typically experienced in purely data-driven predictors. A prediction horizon of 30 seconds ahead of real time during docking operations is examined. History data from the 29meter coastal displacement ship RV (Research Vessel) Gunnerus is applied to validate the approach. Results show that the inclusion of the data-based ML model significantly improves the prediction accuracy.

When a ship experiences a loss of position reference systems, the ship's navigation system typically enters a mode known as dead reckoning to maintain an estimate of the position of the ship. Commercial systems perform this task using a state estimator that includes mathematical model knowledge. Such a model is non-trivial to derive and needs tuning if the dynamic properties of the vessel change. To this end we propose to use machine learning to estimate the horizontal velocity of the vessel without the help of position, velocity or acceleration sensors. A simulation study was conducted to show the ability to maintain position estimates during a Global Navigation Satellite System outage. Comparable performance is seen relative to the established Kalman Filter model-based approach.

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This paper describes a semiglobal exponentially stable output feedback control law for automatic heading control of ships described by a nonlinear model. Only compass (yaw angle) feedback is used. The yaw rate signal is reconstructed by a linear observer. A wave filter is included in the observer to reduce wear and tear on the rudder servo due to first-order wave-induced disturbances. Integral action is included in the control law to compensate for wave drift (second-order wave disturbances), low-frequency wind, and current disturbances. The performance and robustness of the controller are demonstrated in a simulation study of two medium-sized ships.