摘要
为提高校车侧翻预警的精度,提出了基于T-S型模糊神经网络优化TTR的侧翻预警算法。根据校车的车体结构和侧翻机理,建立3自由度侧翻动力学模型,选取优化后的动态横向载荷转移率为校车侧翻稳定性能因子。在传统TTR侧翻预警算法的基础上,引入T-S型模糊神经网络,并选取斜坡等4种转向工况,利用车辆状态参数组合对传统TTR-时间曲线进行优化分析。仿真结果表明,基于T-S型模糊神经网络优化TTR的侧翻预警算法结果准确度高、时效性较好,能够有效地提高校车侧翻预警精度,显著地优化了传统TTR-时间关系曲线,有利于改善校车的行驶安全性。
In order to improve the warning accuracy of school bus rollover, an optimized TTR rollover warning algorithm based on T-S fuzzy neural network is presented in this paper. According to the vehicle structure and rollover mechanism, 3 degrees of freedom dynamic rollover model is established. The optimized dynamic lateral-load transfer rate is chosen as rollover stability factor. T-S fuzzy neural network is introduced based on the traditional TI＇R rollover warning algorithm. Then, typical vehicle state parameters are used to optimize and analyze the traditional TI＇R-Time curve and to carry on the analysis to the slope of four kinds of steering condition as an example. The simulation results show that this algorithm is of high accuracy and good timeliness. It also can effectively improve the precision of the school bus rollover warning,optimize the traditional Tl＇R-Time curve significantly and improve school bus driving safety.
出处
《机械设计》
CSCD
北大核心
2014年第10期44-49,共6页
Journal of Machine Design
基金
国家自然科学基金资助项目(51175448)
河北省自然科学基金资助项目(E2012203071)
流体动力与机电系统国家重点实验室开放基金资助项目(GZKF-201103)
关键词
校车
侧翻预警
T-S模糊神经网络
优化曲线
school bus
rollover warning
T-S fuzzy neural network
curve optimizing
