Cooperative control of leader–follower ships with prescribed performance

With consideration of prescribed performance, the robust cooperative control of leader–follower ships with the existence of ocean disturbances and model uncertainties is studied. The environmental disturbances are modeled as the harmonics disturbances with unknown amplitude and frequency, which are evaluated using an adaptive disturbance observer(ADO). Meanwhile, the model uncertainties is approximated utilizing the neural network, and the prescribed performance method is employed so that the tracking errors can be limited to the predefined set. Then the anti-disturbance cooperative control strategy is presented such that the system states are uniformly ultimately bounded, in addition, the collaborative motion among the leader–follower formation ships are achieved. Eventually, comparative simulations are employed to validate the cooperative control method.

• Record URL:
  • https://doi.org/10.1016/j.oceaneng.2024.119719
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0029801824030579
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Wei, Xinjiang
  • 0000-0001-7475-5658
  • Mu, Chengmei
  • Zhang, Huifeng
  • Hu, Xin
• Publication Date: 2024-12-15

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 119719
• Serial:
  • Ocean Engineering
  • Volume: 314
  • 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: Control systems; Neural networks; Ships; Vehicle performance
• Subject Areas: Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01937478
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 20 2024 11:58AM