基于混沌优化算法的列车动力学模型参数辨识  

Parameter Identification of Train Dynamics Model Based on Chaos Optimization Algorithm

作　　者：刘效睿 刘杨[1] 谭志勇 朱文锋 肖家旭 LIU Xiao-rui;LIU Yang;TAN Zhi-yong;ZHU Wen-feng;XIAO Jia-xu(School of Automation and Electrical Engineering,Dalian Jiaotong University,Dalian 116028 China;Overseas Business Department,CRRC Dalian Co.,Ltd.,Dalian 116022 China)

机构地区：[1]大连交通大学自动化与电气工程学院,辽宁大连116028 [2]中车大连机车车辆有限公司海外事业部,辽宁大连116022

出　　处：《自动化技术与应用》2024年第6期5-9,共5页Techniques of Automation and Applications

基　　金：国家自然科学基金资助项目(62103074);辽宁省本科创新创业培训计划(202110150060);北京交通大学上水平本科教学实验平台开放课题(2021RTCC09)。

摘　　要：针对列车动力学参数未知问题,设计基于混沌优化算法的非线性列车模型参数辨识方法。将高速列车参数估计问题转化为优化问题,给出混沌优化参数估计框架,采用变尺度优化方法,在搜索进程中不断缩小优化变量的搜索空间及二次搜索调节系数,提高搜索精度。计算机仿真验证算法的有效性和可行性,基本混沌优化算法的参数辨识相对误差较遗传算法提高26.8%,基于变尺度混沌优化算法的参数辨识相对误差较基本算法提高了48.7%。Aiming at the unknown problem of train dynamic parameters,a parameter identification method of nonlinear train model based on chaotic optimization algorithm is designed.The parameter estimation problem of high-speed train is transformed into an optimization problem,and the chaotic optimization parameter estimation framework is given.The variable scale optimization method is adopted to continuously reduce the search space of optimization variables and the adjustment coefficient of secondary search in the search process,so as to improve the search accuracy.Computer simulation verifies the effectiveness and feasibility of the algorithm.The relative error of parameter identification of the basic chaotic optimization algorithm is 26.8%higher than that of genetic algorithm,and the relative error of parameter identification based on variable scale chaotic optimization algorithm is 48.7%higher than that of the basic algorithm.

关 键 词：列车模型 参数辨识 混沌算法 优化方法 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]