Barrier function-based adaptive neural network sliding mode control of autonomous surface vehicles

In this paper, the authors consider trajectory tracking control for autonomous surface vehicles (ASVs) with unknown boundary model uncertainties and external disturbances. The neural networks (NNs) and the sliding mode control (SMC) with a switched adaptive law are combined for the first time. The NNs are used to approximate model uncertainties and external disturbances of ASVs. The parameters of the robust SMC term first increase until the sliding variable reaches a quasi sliding mode which bound is associated with the parameter of the barrier function (BF). Then the BF is selected as the parameter of the robust term for the SMC strategy to estimate the NN approximation error and to constrain the sliding variable inside the predefined quasi sliding mode. One salient feature of the authors' approach is that the robust control parameter is no more than approximation error of NNs in sliding and steady phases of SMC. Simulation studies are performed to illustrate the advantage of the proposed control method.

• Record URL:
  https://doi.org/10.1016/j.oceaneng.2021.109684
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S002980182101057X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Yan, Yan
  • Zhao, Xiangtao
  • Yu, Shuanghe
  • Wang, Chaoli
• Publication Date: 2021-10-15

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Serial:
  • Ocean Engineering
  • Volume: 238
  • 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: Autonomous vehicles; Neural networks; Ships; Sliding mode control; Trajectory control
• Subject Areas: Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01781694
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 20 2021 2:52PM