IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v169y2016i2d10.1007_s10957-015-0809-6.html
   My bibliography  Save this article

Potential-Based Least-Squares Policy Iteration for a Parameterized Feedback Control System

Author

Listed:
  • Kang Cheng

    (Southeast University)

  • Kanjian Zhang

    (Southeast University)

  • Shumin Fei

    (Southeast University)

  • Haikun Wei

    (Southeast University)

Abstract

In the paper, a potential-based policy iteration method is proposed for optimal control of a stochastic dynamic system with an average cost criterion and a parameterized control law. In this method, the potential function and the optimal control parameters are obtained via a least-squares-based approach. The potential estimation algorithm is derived from a temporal difference learning method, which can be viewed as a continuous version of the least-squares policy evaluation algorithm. The policy iteration algorithm is validated by solving a linear quadratic gaussian problem in the simulation.

Suggested Citation

  • Kang Cheng & Kanjian Zhang & Shumin Fei & Haikun Wei, 2016. "Potential-Based Least-Squares Policy Iteration for a Parameterized Feedback Control System," Journal of Optimization Theory and Applications, Springer, vol. 169(2), pages 692-704, May.
    • Handle: RePEc:spr:joptap:v:169:y:2016:i:2:d:10.1007_s10957-015-0809-6
    DOI: 10.1007/s10957-015-0809-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10957-015-0809-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10957-015-0809-6?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.

    References listed on IDEAS

    as
    1. Kang Cheng & Shumin Fei & Kanjian Zhang & Xiaomei Liu & Haikun Wei, 2014. "Temporal Difference-Based Policy Iteration for Optimal Control of Stochastic Systems," Journal of Optimization Theory and Applications, Springer, vol. 163(1), pages 165-180, October.
    2. X. R. Cao, 1999. "Single Sample Path-Based Optimization of Markov Chains," Journal of Optimization Theory and Applications, Springer, vol. 100(3), pages 527-548, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kang Cheng & Shumin Fei & Kanjian Zhang & Xiaomei Liu & Haikun Wei, 2014. "Temporal Difference-Based Policy Iteration for Optimal Control of Stochastic Systems," Journal of Optimization Theory and Applications, Springer, vol. 163(1), pages 165-180, October.

    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:169:y:2016:i:2:d:10.1007_s10957-015-0809-6. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.