IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/2026104.html
   My bibliography  Save this article

Bridge-Ship Collision Avoidance Control Based on AFSMC with a FRNN Estimator

Author

Listed:
  • Qionglin Fang
  • Enguang Cao

Abstract

For collision avoidance and maneuvering control in bridge areas, an adaptive fractional sliding mode control with fractional recurrent neural network (FRNN-AFSMC) is proposed. The uncertainties are estimated by FRNN, and the fractional gradient is adopted to improve the recurrent neural network (RNN). Its convergence has been proven. The influence of fractional order on algorithm performance is analyzed, and the simulation platform of ship collision avoidance control is built. Dynamic collision avoidance of multiple ships is simulated and verified. The results show the feasibility and effectiveness of dynamic autonomous collision avoidance motion control in a dynamic ocean environment.

Suggested Citation

  • Qionglin Fang & Enguang Cao, 2021. "Bridge-Ship Collision Avoidance Control Based on AFSMC with a FRNN Estimator," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-13, July.
    • Handle: RePEc:hin:jnlmpe:2026104
    DOI: 10.1155/2021/2026104
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/2026104.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2021/2026104.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/2026104?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:2026104. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.