Robust Parameter Identification Method of Adhesion Model for Heavy Haul Trains

作     者：Shuai Qian Lingshuang Kong Jing He Shuai Qian;Lingshuang Kong;Jing He

作者机构：College of Railway Transportation Hunan University of Technology Zhuzhou China College of Electronic Information and Electrical Engineering Changsha University Changsha China College of Electrical and Information Engineering Hunan University of Technology Zhuzhou China 

出 版 物：《Journal of Transportation Technologies》 (交通科技期刊（英文）)

年 卷 期：2024年第14卷第1期

页      面：53-63页

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

主　　题：Heavy-Duty Train Kiencke Model Quadratic Programming Time-Varying Forgetting Factor Granger Causality Test 

摘      要：A robust parameter identification method based on Kiencke model was proposed to solve the problem of the parameter identification accuracy being affected by the rail environment change and noise interference for heavy-duty trains. Firstly, a Kiencke stick-creep identification model was constructed, and the parameter identification task was transformed into a quadratic programming problem. Secondly, an iterative algorithm was constructed to solve the problem, into which a time-varying forgetting factor was added to track the change of the rail environment, and to solve the uncertainty problem of the wheel-rail environment. The Granger causality test was adopted to detect the interference, and then the weights of the current data were redistributed to solve the problem of noise interference in parameter identification. Finally, simulations were carried out and the results showed that the proposed method could track the change of the track environment in time, reduce the noise interference in the identification process, and effectively identify the adhesion performance parameters.