  Several studies on ship maneuvering models have been conducted using captive
model tests or computational fluid dynamics (CFD) and physical models, such as
the maneuvering modeling group (MMG) model. A new system identification method
for generating a low-speed maneuvering model using recurrent neural networks
(RNNs) and free running model tests is proposed in this study. We especially
focus on a low-speed maneuver such as the final phase in berthing to achieve
automatic berthing control. Accurate dynamic modeling with minimum modeling
error is highly desired to establish a model-based control system. We propose a
new loss function that reduces the effect of the noise included in the training
data. Besides, we revealed the following facts - an RNN that ignores the memory
before a certain time improved the prediction accuracy compared with the
"standard" RNN, and the random maneuver test was effective in obtaining an
accurate berthing maneuver model. In addition, several low-speed free running
model tests were performed for the scale model of the M.V. Esso Osaka. As a
result, this paper showed that the proposed method using a neural network model
could accurately represent low-speed maneuvering motions.
