Research on Train Braking Sliding Mode Control Based on Optimal Creep Rate
The train can not make full use of the adhesion between the wheel and rail during the braking service,which affects the braking performance of the train because track surface change.In order to ensure the steady and safe operation of the train during the braking service and improve the braking performance of the train,a single-axle dynamic model of the train braking was established.A Kalman filter algorithm was used to estimate the wheel-rail adhesion coefficient and an adaptive forgetting factor recursive least squares algorithm is used to estimate the optimal reference creep rate.On this basis,an adaptive sliding mode control algorithm based on RBF neural network is proposed to control the train braking torque.The results show that the control algorithm can not only make the train run in real time near the point of maximum adhesion coefficient of the current track surface,but also make full use of the wheel-rail adhesion to carry out optimal braking control,the chattering problem of the traditional sliding mode control can be reduced,and the optimal adhesion braking control of the train is realized.
brake controladhesionKalman filterleast squaresadaptive sliding mode control
万方数据
维普
