IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/7859521.html
   My bibliography  Save this article

An Improved Rollover Index Based on BP Neural Network for Hydropneumatic Suspension Vehicles

Author

Listed:
  • Xiaotong Dong
  • Yi Jiang
  • Zhou Zhong
  • Wei Zeng
  • Wei Liu

Abstract

The 3-DOF rollover model has been established by the Lagrangian second-class equation, taking the road inclination angle, the steering strategy, and the hydropneumatic suspension characteristics into consideration. A 3-layer BP (backpropagation) neural network is applied to predict the road inclination angle and to optimize the rollover model in real-time. The number of the hidden layer neurons for the BP network is also discussed. The numerical calculation of the optimized rollover model is in good agreement with the full-scale vehicle test. Different rollover indexes are compared, and the results indicate that the rollover index of dynamic LTR optimized by the BP neural network can evaluate the rollover tendency more accurately in the ramp steering test and the snake steering test. This study provides practical meanings for developing a rollover warning system.

Suggested Citation

  • Xiaotong Dong & Yi Jiang & Zhou Zhong & Wei Zeng & Wei Liu, 2018. "An Improved Rollover Index Based on BP Neural Network for Hydropneumatic Suspension Vehicles," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-15, April.
    • Handle: RePEc:hin:jnlmpe:7859521
    DOI: 10.1155/2018/7859521
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2018/7859521.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2018/7859521.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2018/7859521?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:7859521. 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.