IDEAS home Printed from https://ideas.repec.org/a/igg/jcini0/v12y2018i3p1-17.html
   My bibliography  Save this article

Improved State Space Model Using Iterative PSO for Unsteady Aerodynamic System at High AOA

Author

Listed:
  • Guiming Luo

    (School of Software, Tsinghua University, Beijing, China)

  • Boxu Zhao

    (School of Software, Tsinghua University, Beijing, China)

  • Mengqi Jiang

    (School of Software, Tsinghua University, Beijing, China)

Abstract

Due to the complex hysteresis phenomenon at a high angle of attack (AOA), modeling of unsteady aerodynamic coefficients usually encounters the problem that the parameter vector is too long and the simulation accuracy is not high. The article proposes an improved state-space model based on aerodynamics, applying Fourier analysis and the principal component analysis for model optimization. The likelihood criterion and GOIPSO (Iterative Particle Swarm Optimization Based on Genetic Operator) algorithm are established under the Gaussian assumption. The iterative PSO, into which the genetic algorithm's operators are integrated to calculate the optimization of the likelihood function, greatly reduced the probability of local optimization. Experiments show that the algorithm and model proposed in this paper greatly improves the model-fitting accuracy.

Suggested Citation

  • Guiming Luo & Boxu Zhao & Mengqi Jiang, 2018. "Improved State Space Model Using Iterative PSO for Unsteady Aerodynamic System at High AOA," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 12(3), pages 1-17, July.
    • Handle: RePEc:igg:jcini0:v:12:y:2018:i:3:p:1-17
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCINI.2018070101
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:igg:jcini0:v:12:y:2018:i:3:p:1-17. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.