IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v108y2001i3d10.1023_a1017539525721.html
   My bibliography  Save this article

Semi-Infinite Programming Approach to Continuously-Constrained Linear-Quadratic Optimal Control Problems

Author

Listed:
  • Y. Liu

    (Hong Kong Polytechnic University)

  • S. Ito

    (Institute of Statistical Mathematics)

  • H. W. J. Lee

    (Hong Kong Polytechnic University)

  • K. L. Teo

    (Hong Kong Polytechnic University)

Abstract

Consider the class of linear-quadratic (LQ) optimal control problems with continuous linear state constraints, that is, constraints imposed on every instant of the time horizon. This class of problems is known to be difficult to solve numerically. In this paper, a computational method based on a semi-infinite programming approach is given. The LQ optimal control problem is formulated as a positive-quadratic infinite programming problem. This can be done by considering the control as the decision variable, while taking the state as a function of the control. After parametrizing the decision variable, an approximate quadratic semi-infinite programming problem is obtained. It is shown that, as we refine the parametrization, the solution sequence of the approximate problems converges to the solution of the infinite programming problem (hence, to the solution of the original optimal control problem). Numerically, the semi-infinite programming problems obtained above can be solved efficiently using an algorithm based on a dual parametrization method.

Suggested Citation

  • Y. Liu & S. Ito & H. W. J. Lee & K. L. Teo, 2001. "Semi-Infinite Programming Approach to Continuously-Constrained Linear-Quadratic Optimal Control Problems," Journal of Optimization Theory and Applications, Springer, vol. 108(3), pages 617-632, March.
  • Handle: RePEc:spr:joptap:v:108:y:2001:i:3:d:10.1023_a:1017539525721
    DOI: 10.1023/A:1017539525721
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1023/A:1017539525721
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1023/A:1017539525721?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.

    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:spr:joptap:v:108:y:2001:i:3:d:10.1023_a:1017539525721. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.