IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i11p1906-d119657.html
   My bibliography  Save this article

Neural Adaptive Sliding-Mode Control of a Vehicle Platoon Using Output Feedback

Author

Listed:
  • Maode Yan

    (School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China)

  • Jiacheng Song

    (School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China)

  • Lei Zuo

    (School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China)

  • Panpan Yang

    (School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China)

Abstract

This paper investigates the output feedback control problem of a vehicle platoon with a constant time headway (CTH) policy, where each vehicle can communicate with its consecutive vehicles. Firstly, based on the integrated-sliding-mode (ISM) technique, a neural adaptive sliding-mode control algorithm is developed to ensure that the vehicle platoon is moving with the CTH policy and full state measurement. Then, to further decrease the measurement complexity and reduce the communication load, an output feedback control protocol is proposed with only position information, in which a higher order sliding-mode observer is designed to estimate the other required information (velocities and accelerations). In order to avoid collisions among the vehicles, the string stability of the whole vehicle platoon is proven through the stability theorem. Finally, numerical simulation results are provided to verify its effectiveness and advantages over the traditional sliding-mode control method in vehicle platoons.

Suggested Citation

  • Maode Yan & Jiacheng Song & Lei Zuo & Panpan Yang, 2017. "Neural Adaptive Sliding-Mode Control of a Vehicle Platoon Using Output Feedback," Energies, MDPI, vol. 10(11), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1906-:d:119657
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/11/1906/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/11/1906/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Wang, Shu-Tong & Zhu, Wen-Xing & Ma, Xiao-Long, 2023. "Mixed traffic system with multiple vehicle types and autonomous vehicle platoon: Modeling, stability analysis and control strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    2. Jiří David & Pavel Brom & František Starý & Josef Bradáč & Vojtěch Dynybyl, 2021. "Application of Artificial Neural Networks to Streamline the Process of Adaptive Cruise Control," Sustainability, MDPI, vol. 13(8), pages 1-25, April.

    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:gam:jeners:v:10:y:2017:i:11:p:1906-:d:119657. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.