Research on Braking Distance Prediction Method of Satellite Car for Tamping Based on BP Neural Network - ZHUO Haijun; XIAO Xin; HU Zhenfan; WANG Jianhong; ZHANG Yanlei (Kongzhi Yu Xinxi Jishu)
To deal with the obstacles in the automatic tamping system of D09-32 type tamping vehicles, such as the complicated braking distance prediction algorithm of satellite car and the challenges in engineering implementation, this paper proposes a braking distance prediction method based on BP neural network. By leveraging the data of real-time speed and position of the satellite car, the braking distance of the satellite car at the current speed and position is predicted by a BP neural network model representing the relationship between car's braking speed and position and its corresponding braking distance. The key model of this prediction method was simulated using Matlab software and the results were compared with the sample values. The comparison results show that the proposed braking distance prediction method based on BP neural network can satisfactorily predict the satellite car's braking distance. Compared with the currently prevailing linear fitting prediction method, the prediction error of this method is reduced by 48.4% on average, which can effectively improve the precision of automatic tamping, thus raising applicability and robustness of the tamping vehicles.