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

Zhiwu Huang, Wei Du, Bin Chen, Kai Gao, Yongjie Liu, Xuanheng Tang and Yingze Yang
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Zhiwu Huang: School of Automation, Central South University, Changsha 410114, China
Wei Du: School of Automation, Central South University, Changsha 410114, China
Bin Chen: School of Automation, Central South University, Changsha 410114, China
Kai Gao: Hunan Engineering Laboratory of Rail Vehicles Braking Technology, Changsha 410114, China
Yongjie Liu: School of Automation, Central South University, Changsha 410114, China
Xuanheng Tang: School of Automation, Central South University, Changsha 410114, China
Yingze Yang: Hunan Engineering Laboratory of Rail Vehicles Braking Technology, Changsha 410114, China

Energies, 2020, vol. 13, issue 7, 1-19

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.

Keywords: anti-slip control; recursive least rquare; differential evolution; super-twisting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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