IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v48y2017i14p3018-3031.html
   My bibliography  Save this article

Adaptive neural tracking control for nonstrict-feedback stochastic nonlinear time-delay systems with full-state constraints

Author

Listed:
  • Wenjie Si
  • Xunde Dong
  • Feifei Yang

Abstract

An adaptive neural tracking control is investigated for a class of nonstrict-feedback stochastic nonlinear time-delay systems with full-state constraints and saturation input. First, the continuous differentiable saturation model is employed to ensure the input constraint, and a barrier Lyapunov function is designed to achieve the full-state constraint. Second, the appropriate Lyapunov–Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown time-delay terms, and neural networks are employed to approximate the unknown nonlinearities. Finally, based on Lyapunov stability theory, an adaptive controller is proposed to guarantee that all the signals in the closed-loop system are 4-Moment (or 2-Moment) semi-globally uniformly ultimately bounded and the tracking error converges to a small neighbourhood of the origin. Two examples are shown to further demonstrate the effectiveness of the proposed control scheme.

Suggested Citation

  • Wenjie Si & Xunde Dong & Feifei Yang, 2017. "Adaptive neural tracking control for nonstrict-feedback stochastic nonlinear time-delay systems with full-state constraints," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(14), pages 3018-3031, October.
    • Handle: RePEc:taf:tsysxx:v:48:y:2017:i:14:p:3018-3031
    DOI: 10.1080/00207721.2017.1367049
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2017.1367049
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2017.1367049?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiao, Wenbin & Cao, Liang & Dong, Guowei & Zhou, Qi, 2019. "Adaptive fuzzy control for pure-feedback systems with full state constraints and unknown nonlinear dead zone," Applied Mathematics and Computation, Elsevier, vol. 343(C), pages 354-371.

    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:taf:tsysxx:v:48:y:2017:i:14:p:3018-3031. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TSYS20 .

    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.