On neural network identification for low-speed ship maneuvering model

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 neur...

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Bibliographic Details
Published in:Journal of marine science and technology 2022-03, Vol.27 (1), p.772-785
Main Authors: Wakita, Kouki, Maki, Atsuo, Umeda, Naoya, Miyauchi, Yoshiki, Shimoji, Tohga, Rachman, Dimas M., Akimoto, Youhei
Format: Article
Language:English
Subjects:
Automatic control
Automotive Engineering
Berthing
Computational fluid dynamics
Computer applications
Control systems
Dynamic models
Engineering
Engineering Design
Engineering Fluid Dynamics
Fluid dynamics
Hydrodynamics
Identification
Identification methods
Low speed
Mechanical Engineering
Model testing
Modelling
Neural networks
Offshore Engineering
Original Article
Recurrent neural networks
Scale models
Ship handling
System identification
Tests
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Summary: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 manual 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.
ISSN:0948-4280
1437-8213
DOI:10.1007/s00773-021-00867-1