IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v49y2018i15p3130-3145.html
   My bibliography  Save this article

Adaptive output feedback quantised tracking control for stochastic nonstrict-feedback nonlinear systems with input saturation

Author

Listed:
  • Yekai Yang
  • Zhaoxu Yu
  • Shugang Li

Abstract

This paper is concerned with the problem of adaptive output feedback quantised tracking control for a class of stochastic nonstrict-feedback nonlinear systems with asymmetric input saturation. Especially, both input and output signals are quantised by two sector-bounded quantisers. In order to solve the technical difficulties originating from asymmetric saturation nonlinearities and sector-bounded quantisation errors, some special technique, approximation-based methods and Gaussian error function-based continuous differentiable model are exploited. Meanwhile, an observer including the quantised input and output signals is designed to estimate the states. Then, a novel output feedback adaptive quantised control scheme is proposed to ensure that all signals in the closed-loop system are 4-moment (2-moment) semi-globally uniformly ultimately bounded while the output signal follows a desired reference signal. Finally, the effectiveness and applicability of the design methodology is illustrated with two simulation examples.

Suggested Citation

  • Yekai Yang & Zhaoxu Yu & Shugang Li, 2018. "Adaptive output feedback quantised tracking control for stochastic nonstrict-feedback nonlinear systems with input saturation," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(15), pages 3130-3145, November.
  • Handle: RePEc:taf:tsysxx:v:49:y:2018:i:15:p:3130-3145
    DOI: 10.1080/00207721.2018.1534025
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2018.1534025
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207721.2018.1534025?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. Byung Mo Kim & Sung Jin Yoo, 2021. "Approximation-Based Quantized State Feedback Tracking of Uncertain Input-Saturated MIMO Nonlinear Systems with Application to 2-DOF Helicopter," Mathematics, MDPI, vol. 9(9), pages 1-16, May.

    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:49:y:2018:i:15:p:3130-3145. 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.