Heavy-load train model based on RBF neural network refers to the adaptive brake control strategy

Abstract

Heavy-load train braking control is an important automatic driving function. The braking mode mainly used in China is air braking, which has great nonlinear characteristics. In order to reduce the influence of system uncertainty on control, improve the control accuracy and ensure the safety and efficiency of train transportation, an adaptive brake control strategy is designed.Firstly, establish the state space model of braking system according to the dynamic process of overloaded train braking, use the adaptive method and design the RBF model to track the train speed. Finally, the actual data from CK1466.1 to CK1431.3 in the line is used as the reference model. Experimental results The RBF neural network model designed by the reference adaptive controller realizes the tracking control of a given speed curve with high accuracy, verifying the effectiveness of the strategy.