IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v50y2019i6p1275-1289.html
   My bibliography  Save this article

Data-driven optimal control of switched linear autonomous systems

Author

Listed:
  • Chi Zhang
  • Minggang Gan
  • Jingang Zhao

Abstract

In this paper, a novel data-driven optimal control approach of switching times is proposed for unknown continuous-time switched linear autonomous systems with a finite-horizon cost function and a prescribed switching sequence. No a priori knowledge on the system dynamics is required in this approach. First, some formulas based on the Taylor expansion are deduced to estimate the derivatives of a cost function with respect to the switching times using system state data. Then, a data-driven optimal control approach based on the gradient decent algorithm is designed, taking advantage of the derivatives to approximate the optimal switching times. Moreover, the estimation errors are analysed and proven to be bounded. Finally, simulation examples are illustrated to validate the effectiveness of the approach.

Suggested Citation

  • Chi Zhang & Minggang Gan & Jingang Zhao, 2019. "Data-driven optimal control of switched linear autonomous systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(6), pages 1275-1289, April.
    • Handle: RePEc:taf:tsysxx:v:50:y:2019:i:6:p:1275-1289
    DOI: 10.1080/00207721.2019.1598512
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2019.1598512
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2019.1598512?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.

    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:50:y:2019:i:6:p:1275-1289. 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.