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

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.