Real-time wheel-rail adhesion anti-skid control model for high-speed articulated trains on long downhill slopes during braking

To improve the braking efficiency of articulated trains on long-steep downhill gradients under different wheel-rail adhesion levels, this paper develops a real-time train braking control system equipped with an optimal adhesion anti-slip controller. Simulation validation is conducted under both dry and wet wheel-rail contact conditions with different braking torques applied on a steep downhill slope. The anti-slip adhesion control system comprises two modules: an adhesion optimization module using a forgetting factor recursive least squares (FFRLS) method to determine creep force function slopes for enhancing wheel-rail adhesion utilization, and a controller employing a neural network with a Levenberg-Marquardt (L-M) algorithm. Through the co-simulation of the train-track coupling dynamic model and the anti-skid control system, the real-time adjustment of the braking torque of the train can be quickly corrected. The results indicate that the anti-skid control system demonstrates excellent self-adjustment capabilities under various operating conditions. It effectively controls wheel slip, achieving optimal wheel-rail adhesion. This ensures the shortest possible braking distance while maintaining train stability and improving train transmission efficiency.