IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i19p3178-d1764605.html
   My bibliography  Save this article

Trajectory Tracking of WMR with Neural Adaptive Correction

Author

Listed:
  • Sahbi Boubaker

    (Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia)

  • Jeremias Gaia

    (Instituto de Automatica, UNSJ-CONICET, San Juan CP 5400, Argentina)

  • Eduardo Zavalla

    (Instituto de Automatica, UNSJ-CONICET, San Juan CP 5400, Argentina)

  • Souad Kamel

    (Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia)

  • Faisal S. Alsubaei

    (Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah 23218, Saudi Arabia)

  • Farid Bourennani

    (Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah 23218, Saudi Arabia)

  • Francisco Rossomando

    (Instituto de Automatica, UNSJ-CONICET, San Juan CP 5400, Argentina)

Abstract

Wheeled mobile robots (WMRs) are being increasingly integrated into various sectors such as logistics and transportation. However, their accurate trajectory tracking remains a challenge. To address this control issue, this study proposes a trajectory correction technique for a wheeled mobile robot (WMR). This proposal uses a functional-link neural network (FLNN) that adjusts the trajectory error with the aim of minimizing it. This error is propagated backward by adjusting the different parameters of the controller. The controller was designed using a combination of linearization feedback, sliding mode control, and FLNN, where the latter provides adaptability to the controller. Using the Lyapunov stability theory, the stability of the proposal was demonstrated. Experiments and simulation analyses were also carried out to demonstrate the practical feasibility of the proposal.

Suggested Citation

  • Sahbi Boubaker & Jeremias Gaia & Eduardo Zavalla & Souad Kamel & Faisal S. Alsubaei & Farid Bourennani & Francisco Rossomando, 2025. "Trajectory Tracking of WMR with Neural Adaptive Correction," Mathematics, MDPI, vol. 13(19), pages 1-22, October.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:19:p:3178-:d:1764605
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/19/3178/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/19/3178/
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:gam:jmathe:v:13:y:2025:i:19:p:3178-:d:1764605. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.