IDEAS home Printed from https://ideas.repec.org/a/hin/jnddns/7259613.html
   My bibliography  Save this article

Adaptive Neural Network Control Scheme of Switched Systems with Input Saturation

Author

Listed:
  • Xiaoli Jiang
  • Mingyue Liu
  • Siqi Liu
  • Jing Xu
  • Lina Liu

Abstract

This paper investigates a scheme of adaptive neural network control for a stochastic switched system with input saturation. The unknown smooth nonlinear functions are approximated directly by neural networks. A modified approach is proposed to deal with unknown functions with nonstrict feedback form in the design process. Furthermore, by combining the auxiliary design signal and the adaptive backstepping design, a valid adaptive neural tracking controller design algorithm is presented such that all the signals of the switched closed-loop system are in probability semiglobally, uniformly, and ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in probability. In the end, the effectiveness of the proposed method is verified by a simulation example.

Suggested Citation

  • Xiaoli Jiang & Mingyue Liu & Siqi Liu & Jing Xu & Lina Liu, 2020. "Adaptive Neural Network Control Scheme of Switched Systems with Input Saturation," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-12, September.
    • Handle: RePEc:hin:jnddns:7259613
    DOI: 10.1155/2020/7259613
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/DDNS/2020/7259613.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/DDNS/2020/7259613.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2020/7259613?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:jnddns:7259613. 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.