IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v60y2002i3p315-324.html
   My bibliography  Save this article

A neural predictive controller for non-linear systems

Author

Listed:
  • Lazar, Mircea
  • Pastravanu, Octavian

Abstract

Design and implementation are studied for a neural network-based predictive controller meant to govern the dynamics of non-linear processes. The advantages of using neural networks for modeling non-linear processes are shown together with the construction of neural predictors. The resulting implementation of the neural predictive controller is able to eliminate the most significant obstacles encountered in non-linear predictive control applications by facilitating the development of non-linear models and providing a rapid, reliable solution to the control algorithm. Controller design and implementation are illustrated for a plant frequently referred to in the literature. Results are given for simulation experiments, which demonstrate the effectiveness of the proposed approach.

Suggested Citation

  • Lazar, Mircea & Pastravanu, Octavian, 2002. "A neural predictive controller for non-linear systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 60(3), pages 315-324.
    • Handle: RePEc:eee:matcom:v:60:y:2002:i:3:p:315-324
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037847540200023X
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Ibrahem, Ibrahem M.A. & Akhrif, Ouassima & Moustapha, Hany & Staniszewski, Martin, 2021. "Nonlinear generalized predictive controller based on ensemble of NARX models for industrial gas turbine engine," Energy, Elsevier, vol. 230(C).
    2. Sun, Kai & Tseng, Chen-Ting & Shan-Hill Wong, David & Shieh, Shyan-Shu & Jang, Shi-Shang & Kang, Jia-Lin & Hsieh, Wei-Dong, 2015. "Model predictive control for improving waste heat recovery in coke dry quenching processes," Energy, Elsevier, vol. 80(C), pages 275-283.

    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:eee:matcom:v:60:y:2002:i:3:p:315-324. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.