Title: Research on the online parameter identification method of train driving dynamic model

Authors: Dandan Liu; Xiangxian Chen; Zhonghao Guo; Jiaxi Yuan; Shoulin Yin

Addresses: College of Biomedical Engineering and Instrument Science, Zhejiang University, China; Research and Development Center, Zhejiang United Science and Technology Co., Ltd., 310018, Hangzhou, China ' College of Biomedical Engineering and Instrument Science, Zhejiang University, China ' College of Biomedical Engineering and Instrument Science, Zhejiang University, China ' College of Biomedical Engineering and Instrument Science, Zhejiang University, China ' Software College, Shenyang Normal University, 110034, Shenyang, China

Abstract: Automatic train operation (ATO) system is an important driving control system for train operation, which adjusts traction or braking force in real time according to different operating environments. As an important part of the ATO system, the train dynamic model determines the tracking accuracy of the train to the target speed. Based on the force analysis of the actual train operation, the single-particle dynamic models of train operation were established. Considering the high efficiency of the single-particle model in online identification, the single-particle train model is applied to the actual parameter identification. Firstly, the second-order single particle model is established, and three identification methods and two sets of data are compared and analysed. The auxiliary model and the recursive least square method with variable forgetting factor (AM-VFF-RLS) identification method have good performance. On this basis, a third-order-single-particle model is established. Through the analysis of the identification results, it is found that the model can improve the identification accuracy while ensuring the efficiency.

Keywords: train dynamic model; online identification; AM-VFF-RLS; ATO system.

DOI: 10.1504/IJCVR.2023.133133

International Journal of Computational Vision and Robotics, 2023 Vol.13 No.5, pp.497 - 509

Received: 13 Apr 2022
Accepted: 27 Apr 2022
Published online: 01 Sep 2023 *

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