IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v160y2014i1d10.1007_s10957-012-0201-8.html
   My bibliography  Save this article

Output Feedback Model Predictive Tracking Control Using a Slope Bounded Nonlinear Model

Author

Listed:
  • S. M. Lee

    (Daegu University)

  • O. M. Kwon

    (Chungbuk National University)

  • Ju H. Park

    (Yeungnam University)

Abstract

In this paper, an output feedback model predictive tracking control method is proposed for constrained nonlinear systems, which are described by a slope bounded model. In order to solve the problem, we consider the finite horizon cost function for an off-set free tracking control of the system. For reference tracking, the steady state is calculated by solving by quadratic programming and a nonlinear estimator is designed to predict the state from output measurements. The optimized control input sequences are obtained by minimizing the upper bound of the cost function with a terminal weighting matrix. The cost monotonicity guarantees that tracking and estimation errors go to zero. The proposed control law can easily be obtained by solving a convex optimization problem satisfying several linear matrix inequalities. In order to show the effectiveness of the proposed method, a novel slope bounded nonlinear model-based predictive control method is applied to the set-point tracking problem of solid oxide fuel cell systems. Simulations are also given to demonstrate the tracking performance of the proposed method.

Suggested Citation

  • S. M. Lee & O. M. Kwon & Ju H. Park, 2014. "Output Feedback Model Predictive Tracking Control Using a Slope Bounded Nonlinear Model," Journal of Optimization Theory and Applications, Springer, vol. 160(1), pages 239-254, January.
    • Handle: RePEc:spr:joptap:v:160:y:2014:i:1:d:10.1007_s10957-012-0201-8
    DOI: 10.1007/s10957-012-0201-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10957-012-0201-8
    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/s10957-012-0201-8?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Soroush Sadeghnejad & Farshad Khadivar & Mojtaba Esfandiari & Golchehr Amirkhani & Hamed Moradi & Farzam Farahmand & Gholamreza Vossoughi, 2023. "Using an Improved Output Feedback MPC Approach for Developing a Haptic Virtual Training System," Journal of Optimization Theory and Applications, Springer, vol. 198(2), pages 745-766, August.

    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:160:y:2014:i:1:d:10.1007_s10957-012-0201-8. 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.