IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-0-387-89496-6_16.html
   My bibliography  Save this book chapter

Online Adaptive Optimal Control Based on Reinforcement Learning

In: Optimization and Optimal Control

Author

Listed:
  • Draguna Vrabie

    (Automation and Robotics Research Institute, University of Texas at Arlington)

  • Frank Lewis

    (Automation and Robotics Research Institute, University of Texas at Arlington)

Abstract

Summary In this chapter a new online direct adaptive scheme is presented which converges to the optimal state feedback control solution for affine in the inputs nonlinear systems. The optimal control solution is obtained in a direct fashion, without system identification. The optimal adaptive control algorithm is derived in a continuous-time framework. The algorithm is an online approach to policy iterations based on an adaptive critic structure to find an approximate solution to the state feedback, infinite-horizon, optimal control problem.

Suggested Citation

  • Draguna Vrabie & Frank Lewis, 2010. "Online Adaptive Optimal Control Based on Reinforcement Learning," Springer Optimization and Its Applications, in: Altannar Chinchuluun & Panos M. Pardalos & Rentsen Enkhbat & Ider Tseveendorj (ed.), Optimization and Optimal Control, pages 309-323, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-89496-6_16
    DOI: 10.1007/978-0-387-89496-6_16
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spochp:978-0-387-89496-6_16. 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.