Black-box modeling of ship maneuvering motion based on multi-output nu-support vector regression with random excitation signal

This paper proposes a novel method for offline black-box modeling of ship maneuvering by utilizing the training data from random maneuvers under medium rudder angle with random amplitude and duration. The identification algorithm adopted is a multi-output ν(‘nu’)-Support Vector Regression, MO-ν-SVR, that has higher computational efficiency and better operability than a conventional ν-SVR. The ONRT vessel is taken as the study object, and numerical simulations are conducted to provide the training, validation and testing datasets. The superiority of the proposed random maneuver over the standard zig-zag maneuver is demonstrated by a contrastive study where the excitation signals from the random maneuver and the 20°/20° zig-zag maneuver are used for training the model separately. To examine the robustness of the proposed modeling method and the identified model, three levels of white noise are added into the raw simulation data for training the model. To explore the effectiveness and generalization ability of the identified model on different motion patterns of ship maneuvering, course-keeping, course-changing, and turning motions are examined separately. The results demonstrate that the model trained by the excitation signals of the random maneuver has better generalization ability and robustness, verifying the feasibility and practicality of the proposed modeling method.

• Record URL:
  • https://doi.org/10.1016/j.oceaneng.2022.111279
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0029801822006734
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhang, Yan-Yun
  • Wang, Zi-Hao
  • 0000-0002-0035-1789
  • Zou, Zao-Jian
• Publication Date: 2022-8-1

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 111279
• Serial:
  • Ocean Engineering
  • Volume: 257
  • Issue Number: 0
  • Publisher: Pergamon
  • ISSN: 0029-8018
  • EISSN: 1873-5258
  • Serial URL: http://www.sciencedirect.com/science/journal/00298018

Subject/Index Terms

• TRT Terms: Machine learning; Maneuvering; Regression analysis; Ships
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01849149
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 23 2022 9:16AM