Road roughness identification based on vehicle responses

被引：0
作者：
Li J. [1 ]
Guo W.-C. [1 ]
Zhao Q. [1 ]
Gu S.-F. [1 ]
机构：
[1] State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2019年 / 49卷 / 06期
关键词：
NARX neural network; Road roughness identification; Vehicle engineering; Vehicle response;
D O I：
10.13229/j.cnki.jdxbgxb20181122
中图分类号：
学科分类号：
摘要：
To solve the problem of road roughness identification, a NARX neural network identification method and its applicability are studied based on vehicle responses. A four degree of freedom plane model of vehicle vibration system is established, thus, the vehicle responses and road roughness of wheel can be obtained by simulation. The application selection, input scheme optimization and evaluation index of NARX neural network are studied, and the solutions of vehicle response selection and its combination optimization are put forward. The NARX neural network is used to identify the road roughness at front wheel of a car under the common road grade B and 60 km/h driving speed, for which the correlation coefficient and root mean square error are 96.75% and 0.003 3, respectively. The influences of training sampling points, vehicle response random noise, vehicle speed, and road grade on the NARX neural network are considered, and the adaptability of NARX neural network method for road roughness identification based on vehicle responses is illustrated. The results show that the use of orthogonal test design to determine the optimal input scheme of the NARX neural network and the identification of road roughness based on vehicle responses can achieve satisfactory performance and good applicability. © 2019, Jilin University Press. All right reserved.
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页码：1810 / 1817
页数：7
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