IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v51y2020i14p2690-2702.html
   My bibliography  Save this article

An iterative algorithm for coupled Riccati equations in continuous-time Markovian jump linear systems

Author

Listed:
  • Ai-Guo Wu
  • Hui-Jie Sun
  • Wanquan Liu

Abstract

In this paper, a novel implicit iterative algorithm with some tuning parameters is developed to solve the coupled algebraic Riccati matrix equation arising in the continuous-time Markovian jump linear systems. By introducing some tuning parameters in the proposed iterative algorithm, the current estimation for unknown variables is updated by using the information not only in the last step but also in the current iterative step and previous iterative steps. These tuning parameters can be appropriately chosen such that the proposed algorithm has faster convergence performance than some previous algorithms. It is shown that the proposed algorithm with zero initial conditions can monotonically converge to the unique positive semidefinite solution of the coupled Riccati matrix equation if the corresponding Markovian jump system is stabilisable. Finally, an example is provided to show the effectiveness of the developed algorithm.

Suggested Citation

  • Ai-Guo Wu & Hui-Jie Sun & Wanquan Liu, 2020. "An iterative algorithm for coupled Riccati equations in continuous-time Markovian jump linear systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(14), pages 2690-2702, October.
    • Handle: RePEc:taf:tsysxx:v:51:y:2020:i:14:p:2690-2702
    DOI: 10.1080/00207721.2020.1800130
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2020.1800130
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2020.1800130?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:51:y:2020:i:14:p:2690-2702. 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.