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An Online Super-Twisting Sliding Mode Anti-Slip Control Strategy

Author

Listed:
  • Zhiwu Huang

    (School of Automation, Central South University, Changsha 410114, China
    Hunan Engineering Laboratory of Rail Vehicles Braking Technology, Changsha 410114, China)

  • Wei Du

    (School of Automation, Central South University, Changsha 410114, China
    Hunan Engineering Laboratory of Rail Vehicles Braking Technology, Changsha 410114, China)

  • Bin Chen

    (School of Automation, Central South University, Changsha 410114, China
    Hunan Engineering Laboratory of Rail Vehicles Braking Technology, Changsha 410114, China)

  • Kai Gao

    (Hunan Engineering Laboratory of Rail Vehicles Braking Technology, Changsha 410114, China
    College of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410114, China)

  • Yongjie Liu

    (School of Automation, Central South University, Changsha 410114, China
    Hunan Engineering Laboratory of Rail Vehicles Braking Technology, Changsha 410114, China)

  • Xuanheng Tang

    (School of Automation, Central South University, Changsha 410114, China
    Hunan Engineering Laboratory of Rail Vehicles Braking Technology, Changsha 410114, China)

  • Yingze Yang

    (Hunan Engineering Laboratory of Rail Vehicles Braking Technology, Changsha 410114, China
    School of Computer Science and Engineering, Central South University, Changsha 410114, China)

Abstract

The variability of rail surfaces can result in wheel–rail slippage, which reduces the accuracy of subway braking systems, or even endangers the operation safety. It is necessary to conduct optimal anti-slip control with the estimation of the wheel–rail adhesion state. In this paper, an online super-twisting sliding mode anti-slip control strategy is proposed for subway vehicles. Firstly, real-time wheel–rail adhesion state estimation is performed by utilizing the recursive least squares algorithm under complex and variable rail surface conditions. Then, the differential evolution algorithm is adopted to search the current optimal slip velocity based on the wheel–rail adhesion state. The super-twisting sliding mode controller is designed to implement the optimal sliding velocity tracking. The controller exploits the high-order derivatives of the sliding mode to eliminate chatter vibration and avoid the effect of disturbance, improving the anti-slip control performance. Finally, the effectiveness of the proposed anti-slip strategy is verified by experimental results.

Suggested Citation

  • Zhiwu Huang & Wei Du & Bin Chen & Kai Gao & Yongjie Liu & Xuanheng Tang & Yingze Yang, 2020. "An Online Super-Twisting Sliding Mode Anti-Slip Control Strategy," Energies, MDPI, vol. 13(7), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1823-:d:343563
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    References listed on IDEAS

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    1. Dejun Yin & Nan Sun & Danfeng Shan & Jia-Sheng Hu, 2017. "A Multiple Data Fusion Approach to Wheel Slip Control for Decentralized Electric Vehicles," Energies, MDPI, vol. 10(4), pages 1-24, April.
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