摘要
提出了一种在转向工况下根据车轮侧偏特性估计路面附着系数的算法。首先在M atlab/S imu link中建立了七自由度整车模型,分析了车轮的侧偏特性;然后设计了扩展卡尔曼滤波器,以根据车辆的纵、侧向加速度,估算车辆的纵、侧向速度,并据此计算出车轮的侧偏角;最后,采用反向传播神经网络算法,根据前轮侧偏角和横摆角速度及其增益,估计路面附着系数。仿真结果验证了该算法的有效性。
In this paper an algorithm for estimating road adhesion coefficient in steering condition is proposed based on wheel cornering characteristics.Firstly,a 7 DOF vehicle model is built with Matlab simulink and the wheel cornering characteristics are analyzed.Then an extended kalman filter is designed to estimate the longitudinal and lateral speeds according to its longitudinal and lateral accelerations,and the side slip angles of wheels are calculated.Finally,the BP neural network algorithm is adopted to estimate road adhesion coefficient based on the side slip angles of two front wheels and the yaw rate and its gain of vehicle.The results of simulation verify the effectiveness of the algorithm.
出处
《汽车工程》
EI
CSCD
北大核心
2011年第6期521-526,共6页
Automotive Engineering
基金
国家自然科学基金项目(50975071)资助
关键词
车轮侧偏角
路面附着系数
扩展卡尔曼滤波器
神经网络
wheel side slip angle
road adhesion coefficient
extended kalman filter
neural network
