Data Augmentation Methods of Dynamic Model Identification for Harbor Maneuvers using Feedforward Neural Network
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866915031777542144 |
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| author | Wakita, Kouki Miyauchi, Yoshiki Akimoto, Youhei Maki, Atsuo |
| author_facet | Wakita, Kouki Miyauchi, Yoshiki Akimoto, Youhei Maki, Atsuo |
| contents | A dynamic model for an automatic berthing and unberthing controller has to estimate harbor maneuvers, which include berthing, unberthing, approach maneuvers to berths, and entering and leaving the port. When the dynamic model is estimated by the system identification, a large number of tests or trials are required to measure the various motions of harbor maneuvers. However, the amount of data that can be obtained is limited due to the high costs and time-consuming nature of full-scale ship trials. In this paper, we improve the generalization performance of the dynamic model for the automatic berthing and unberthing controller by introducing data augmentation. This study used slicing and jittering as data augmentation methods and confirmed their effectiveness by numerical experiments using the free-running model tests. The dynamic model is represented by a neural network-based model in numerical experiments. Results of numerical experiments demonstrated that slicing and jittering are effective data augmentation methods but could not improve generalization performance for extrapolation states of the original dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_18851 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Data Augmentation Methods of Dynamic Model Identification for Harbor Maneuvers using Feedforward Neural Network Wakita, Kouki Miyauchi, Yoshiki Akimoto, Youhei Maki, Atsuo Systems and Control A dynamic model for an automatic berthing and unberthing controller has to estimate harbor maneuvers, which include berthing, unberthing, approach maneuvers to berths, and entering and leaving the port. When the dynamic model is estimated by the system identification, a large number of tests or trials are required to measure the various motions of harbor maneuvers. However, the amount of data that can be obtained is limited due to the high costs and time-consuming nature of full-scale ship trials. In this paper, we improve the generalization performance of the dynamic model for the automatic berthing and unberthing controller by introducing data augmentation. This study used slicing and jittering as data augmentation methods and confirmed their effectiveness by numerical experiments using the free-running model tests. The dynamic model is represented by a neural network-based model in numerical experiments. Results of numerical experiments demonstrated that slicing and jittering are effective data augmentation methods but could not improve generalization performance for extrapolation states of the original dataset. |
| title | Data Augmentation Methods of Dynamic Model Identification for Harbor Maneuvers using Feedforward Neural Network |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2305.18851 |