Learning Nonlinear Dynamics of Ocean Surface Vessel With Multistep Constraints

Toward autonomy in marine vehicles, a high-quality dynamic model is the first step before robust controls and should be properly addressed. However, vessels operating on the ocean surface are subjected to continuous disturbances, and the maneuvering dynamics exhibit a high degree of complexity and nonlinearity. In this study, a learning-based model is proposed to capture the ship maneuvering dynamics along with temporal variations. By taking the time series of ship maneuver commands as input, the model directly predicts the corresponding motions sequence, considering the presence of disturbances. The proposed neural network model is formulated as time-discretized ODEs, and model training is facilitated by posing multistep constraints. Extensive full-scale ship maneuvering experiments are conducted in the open sea to validate the effectiveness. Comparative experiments against existing learning-based models demonstrate improved accuracy in estimating ship velocities and positions over multistep intervals.