基于车辆响应的路面不平度识别方法

Road roughness identification based on vehicle responses

针对路面不平度识别的问题,研究了基于车辆响应的NARX神经网络识别方法及其适用性。建立了汽车振动系统4自由度平面模型,通过仿真获得车辆响应和车轮路面不平度。对于NARX神经网络及其应用选择、输入方案优化和评价指标进行了研究,提出了车辆响应选择和组合优化的解决方案。采用NARX神经网络识别了常用的B级路面和车速为60 km/h下某轿车的前轮路面不平度,其相关系数和均方根误差分别达到96.75%和0.0033。考虑了训练采样点数、车辆响应随机噪声、车速和路面等级的变化对训练完成的NARX神经网络效果的影响,说明了基于车辆响应识别路面不平度的NARX神经网络方法的适用性。研究结果表明,采用正交试验设计确定NARX神经网络优化输入方案和基于车辆响应识别路面不平度取得了满意的结果,两者具有良好的适用性。

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 0.0033,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.