基于神经网络和侧翻时间算法的轻型汽车侧翻预警

›› 2012, Vol. 42 ›› Issue (05): 1083-1088.

基于神经网络和侧翻时间算法的轻型汽车侧翻预警

赵健¹, 郭俐彤¹, 朱冰^1,2, 黄庆玲³, 李世超³, 周欣³

1. 吉林大学汽车仿真与控制国家重点实验室,长春 130022;
2. 吉林大学工程仿生教育部重点实验室,长春 130022;
3. 一汽轿车股份有限公司产品部,长春 130011

收稿日期:2011-11-27 出版日期:2012-09-01 发布日期:2012-09-01
通讯作者: 朱冰(1982-),男,讲师,博士.研究方向:汽车地面系统分析与控制,工程仿生学. E-mail:zhubing@jlu.edu.cn E-mail:zhubing@jlu.edu.cn
基金资助:
国家自然科学基金项目(51105169);吉林省青年科研基金项目(201101028);中国博士后科学基金项目(2011M500053);吉林大学基本科研业务费项目.

Rollover warning for light vehicles based on NN-TTR algorithm

ZHAO Jian¹, GUO Li-tong¹, ZHU Bing^1,2, HUANG Qing-ling³, LI Shi-chao³, ZHOU Xin³

1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China;
2. Key Laboratory of Bionic Engineering of Ministry of Education, Jilin University, Changchun 130022, China;
3. Product Development Department, FAW CAR Co., Ltd., Changchun 130011, China

Received:2011-11-27 Online:2012-09-01 Published:2012-09-01

摘要/Abstract

摘要： 为实现车辆侧翻精确预警,建立了三自由度侧翻模型和基于侧翻时间(TTR)算法的侧翻预警算法。在此基础上引入仿生智能神经网络,提出基于多状态参量修正的NN-TTR侧翻预警算法。选取典型车辆状态参数组合对传统TTR进行修正分析,并利用Matlab/Simulink与Carsim联合仿真平台对侧翻预警算法进行验证。结果表明,基于NN-TTR的轻型汽车侧翻预警算法能够有效提高侧翻预警精度,为主动防侧翻控制奠定良好基础。

关键词: 车辆工程, 侧翻预警, 神经网络, 侧翻时间, 轻型汽车

Abstract: In order to realize the accurate vehicle rollover warning, a 3-DoF rollover model and a time-to-rollover(TTR)-based rollover algorithm was established. Bionic intelligent artificial neural network(NN) was introduced to present a multi-parameter modified NN-TTR rollover warning algorithm. The typical vehicle state parameters were selected to analyze and correct the traditional TTR. The NN-TTR algorithm was validated using Matlab/Simulink and Carsim co-simulation platform. The results showed that the light vehicle rollover warning algorithm based on NN-TTR effectively improves the warning accuracy and provides a good foundation to improve the active anti-rollover control.

Key words: vehicle engineering, rollover warning, neural network, time-to-rollover, light vehicle

中图分类号:

U463.1

赵健, 郭俐彤, 朱冰, 黄庆玲, 李世超, 周欣. 基于神经网络和侧翻时间算法的轻型汽车侧翻预警[J]. , 2012, 42(05): 1083-1088.

ZHAO Jian, GUO Li-tong, ZHU Bing, HUANG Qing-ling, LI Shi-chao, ZHOU Xin. Rollover warning for light vehicles based on NN-TTR algorithm[J]. , 2012, 42(05): 1083-1088.

参考文献

[1] NHTSA:national highway traffic safety administration. Traffic safety facts 2006: a compilation of motor vehicle crash data from the fatality analysis reporting system and general estimates system.. http://www-nrd.nhtsa.dot.gov/Pubs/810818.pdf.
[2] Yim Seongjin, Park Youngjin. Design of rollover prevention controller with linear matrix inequality-based trajectory sensitivity minimization[J]. Vehicle System Dynamics, 2011, 49(8): 1225-1244.
[3] Zhang Nong, Dong Guang-ming, Du Hai-ping. Investigation into untripped rollover of light vehicle in the modified fishhook and the sine maneuver, part I: vehicle modelling, roll and yaw instability[J]. Vehicle System Dynamics, 2008, 46(4): 272-274.
[4] Transportation Research Institute, University of Michigan. Cooperative agreement to foster the deployment of a heavy vehicle intelligent dynamic stability enhancement system interim report. Ann Arbor: University of Michigan, 1998.
[5] Chen Bo-chiuan. Warning and control for vehicle rollover prevention. Ann Arbor: University of Michigan, 2001.
[6] 朱天军,宗长富,吴炳胜,等. 基于改进TTR算法的重型车辆侧翻预警系统[J]. 机械工程学报,2011, 47(10): 88-94. Zhu Tian-jun, Zong Chang-fu, Wu Bing-sheng, et al. Rollover warning system of heavy duty vehicle based on improved TTR algorithm[J]. Journal of Mechanical Engineering, 2011, 47(10): 88-94.
[7] Preston-Thomas J, Woodrooffe J H F. A feasibility study of a rollover warning device for heavy trucks. Transport Canada Publication, No.TP 10610E, 1990.
[8] Braghin F, Cheli F, Corradi R, et al. Active anti-rollover system for heavy-duty road vehicles[J]. Vehicle System Dynamics, 2008, 46(S1): 653-668.
[9] 麦莉,曲伟,朱天军,等. 基于模型预测的重型商用车侧翻预警算法[J]. 吉林大学学报:工学版,2010,40(4):906-910. Mai Li, Qu Wei, Zhu Tian-jun, et al. Rollover warning algorithm of heavy commercial vehicle based on model prediction[J]. Journal of Jilin University (Engineering and Technology Edition), 2010, 40(4): 906-910.

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