IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/3107910.html
   My bibliography  Save this article

Vehicle Sideslip Angle Estimation Based on General Regression Neural Network

Author

Listed:
  • Wang Wei
  • Bei Shaoyi
  • Zhang Lanchun
  • Zhu Kai
  • Wang Yongzhi
  • Hang Weixing

Abstract

Aiming at the accuracy of estimation of vehicle’s mass center sideslip angle, an estimation method of slip angle based on general regression neural network (GRNN) and driver-vehicle closed-loop system has been proposed: regarding vehicle’s sideslip angle as time series mapping of yaw speed and lateral acceleration; using homogeneous design project to optimize the training samples; building the mapping relationship among sideslip angle, yaw speed, and lateral acceleration; at the same time, using experimental method to measure vehicle’s sideslip angle to verify validity of this method. Estimation results of neural network and real vehicle experiment show the same changing tendency. The mean of error is within 10% of test result’s amplitude. Results show GRNN can estimate vehicle’s sideslip angle correctly. It can offer a reference to the application of vehicle’s stability control system on vehicle’s state estimation.

Suggested Citation

  • Wang Wei & Bei Shaoyi & Zhang Lanchun & Zhu Kai & Wang Yongzhi & Hang Weixing, 2016. "Vehicle Sideslip Angle Estimation Based on General Regression Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-7, August.
    • Handle: RePEc:hin:jnlmpe:3107910
    DOI: 10.1155/2016/3107910
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2016/3107910.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2016/3107910.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2016/3107910?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
    ---><---

    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:hin:jnlmpe:3107910. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.