MULTI-OBJECTIVE OPTIMIZATION OF VEHICLE/TRACK PARAMETERS BASED ON RBF NEURAL NETWORK SURROGATE MODEL

XIAO Qian; LUO Chao; OUYANG ZhiXu; CHANG Chao; LUO JiaWen

doi:10.16579/j.issn.1001.9669.2021.02.011

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MULTI-OBJECTIVE OPTIMIZATION OF VEHICLE/TRACK PARAMETERS BASED ON RBF NEURAL NETWORK SURROGATE MODEL

更新时间：2022-09-22

- MULTI-OBJECTIVE OPTIMIZATION OF VEHICLE/TRACK PARAMETERS BASED ON RBF NEURAL NETWORK SURROGATE MODEL
- Journal of Mechanical Strength Vol. 43, Issue 2, Pages: 319-326(2021)
- 作者机构：
  
  1. 华东交通大学载运工具与装备教育部重点实验室
  2. 中车青岛四方机车车辆股份有限公司
  3. 西南交通大学牵引动力国家重点实验室
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2021.02.011
  CLC：
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XIAO Qian, LUO Chao, OUYANG ZhiXu, et al. MULTI-OBJECTIVE OPTIMIZATION OF VEHICLE/TRACK PARAMETERS BASED ON RBF NEURAL NETWORK SURROGATE MODEL. [J]. 43(2):319-326(2021)
DOI：

XIAO Qian, LUO Chao, OUYANG ZhiXu, et al. MULTI-OBJECTIVE OPTIMIZATION OF VEHICLE/TRACK PARAMETERS BASED ON RBF NEURAL NETWORK SURROGATE MODEL. [J]. 43(2):319-326(2021) DOI： 10.16579/j.issn.1001.9669.2021.02.011.

摘要

为探究车辆与轨道参数多目标优化问题,基于RBF(Radial Basis Function)神经网络代理模型对车辆/轨道参数实现多目标优化以改善车辆的动力学性能。通过构建高速列车车辆-轨道耦合动力学仿真模型,借助UM与Isight联合仿真技术分析车辆与轨道参数对动力学性能的灵敏度影响,以灵敏度占比最大的8个参数为设计变量,以动力学性能为响应建立RBF神经网络代理模型,在代理模型的基础上对车辆/轨道参数进行多目标优化。结果表明,车辆与轨道参数在优化后,最优解对脱轨系数的优化率达到13.14%,且对轮重减载率的优化率达到14.63%,可见优化效果显著,车辆的动力学性能获得较大改善。

Abstract

The RBF（ Radial Basis Function） neural network surrogate model that employed to explore the multi-objective optimization problems of vehicle and track parameters is to improve the dynamic performance of vehicles. The sensitivity of dynamic performance on vehicle and track parameters was analyzed by constructing a vehicle-track coupling dynamic simulation model of high-speed train and using the UM and Isight joint simulation technology. The eight parameters with the highest sensitivity ratio were used as the design variables,and a surrogate model of RBF neural network was established on the response of the dynamic performance. Then the model was performed to optimize the vehicle/track parameters. The results show that the optimization rate of the optimal solution for the derailment coefficient is 13. 14%,and the optimization rate of the wheel load reduction rate is 14. 63% after the vehicle and track parameters are optimized,which demonstrates that the optimization effect is remarkable,and the dynamic performance of the vehicle has been significantly improved.

关键词

高速列车代理模型RBF多目标优化灵敏度分析

Keywords

High speed trainAgent modelRadial Basis Function（RBF）Multi-objective optimizationSensitivity analysis

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