IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v468y2024ics0096300323006914.html
   My bibliography  Save this article

Neural network-based robust consensus tracking for uncertain networked Euler-Lagrange systems with time-varying delays and output constraints

Author

Listed:
  • Peng, Runlong
  • Guo, Rongwei
  • Zheng, Bin
  • Miao, Zhonghua
  • Zhou, Jin

Abstract

This paper mainly focuses on the cooperative robust consensus tracking problem of uncertain networked Euler-Lagrange systems (NELSs) with time-varying delays and output constraints. By systematically integrating the neural network (NN) adaptive technique and the logarithmic type Barrier Lyapunov Function (BLF) in combination with the additional robust control law, two distributed robust consensus schemes for uncertain NELSs are proposed for two cases of time-varying communication and input delays respectively, which can fully guarantee to constrain the output consensus error within a safety region simultaneously. Furthermore, numerical simulation examples are provided to demonstrate the comparable potential advantages of the proposed robust control law over some existing algorithms, including adaptability, stability, and robustness, as well as delay effects.

Suggested Citation

  • Peng, Runlong & Guo, Rongwei & Zheng, Bin & Miao, Zhonghua & Zhou, Jin, 2024. "Neural network-based robust consensus tracking for uncertain networked Euler-Lagrange systems with time-varying delays and output constraints," Applied Mathematics and Computation, Elsevier, vol. 468(C).
  • Handle: RePEc:eee:apmaco:v:468:y:2024:i:c:s0096300323006914
    DOI: 10.1016/j.amc.2023.128522
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300323006914
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2023.128522?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:apmaco:v:468:y:2024:i:c:s0096300323006914. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.