基于增广卡尔曼滤波并考虑车辆加速度的路面不平度识别

Road Roughness Identification Based on Augmented Kalman Filtering with Consideration of Vehicle Acceleration

为实现车辆在实际加减速行驶工况下路面不平度的准确识别,提出了一种考虑车辆加速度、基于增广卡尔曼滤波算法的路面识别方法。以车辆纵向加速度作为已知输入,车身垂向振动和俯仰振动响应作为观测向量,设计增广卡尔曼滤波观测器估计路面不平度信息;求取固定位移窗长度内的国际平整度指数,实现了对路面的等级分类。仿真结果表明在典型非匀速工况、城市运行工况和制动工况下,所提出的方法对路面不平度的识别精度和对路面等级分类的准确性,明显高于一般的增广卡尔曼滤波算法,能有效识别未知输入路面。

In order to achieve the accurate identification of road unevenness under actual acceleration and deceleration driving conditions,a road surface identification method based on augmented Kalman filtering algorithm with consideration of vehicle acceleration(AKF-a)is proposed.With the longitudinal acceleration of the vehicle as the known input,and the vertical and pitching vibrations of vehicle body as the observation vectors,the augmented Kalman filter observer is designed to estimate the roughness information of road surface.The international roughness index within the fixed displacement window length is obtained to achieve the grade classification of road surface.The results of simulation show that under typical non-uniform speed conditions,urban operating conditions and braking conditions,the identification accuracy of road unevenness and the correctness of road grade classification with AKF-a algorithm proposed are apparently higher than those with general AKF algorithm and can effectively identify the unknown input road surface.