期刊论文

Estimation Algorithm for Road Adhesion Coefficient Using Adaptive Fading Unscented Kalman Filter

中文

中国公路学报

1001-7372

2020

33

7

176-185

10.19721/j.cnki.1001-7372.2020.07.018

国家自然科学基金项目（11572055）~~；

本校为第一机构

为了获得实时、准确的路面附着系数,进一步提高观测路面附着系数算法的精度和收敛速度,结合非线性车辆动力学模型和轮胎力修正模型,搭建分布式驱动电动汽车联合仿真平台,提出一种基于自适应衰减无迹卡尔曼滤波的路面附着系数观测算法。该算法设计与各轮对应的路面附着系数观测器,应用协方差匹配判据对观测器发散趋势进行判别,设计自适应加权系数修正预测协方差,以增强新近观测数据的利用率;同时采用次优Sage-Husa噪声估计器对未知的系统过程噪声进行估计,抑制观测器的记忆存储长度,调整过程噪声和测量噪声的均值与协方差,提高观测器的跟踪能力。利用分布式驱动电动汽车分别进行高、低附着路面和...

To obtain real-time and accurate road adhesion coefficient and improve the accuracy and convergence speed of the observation algorithm for road adhesion coefficient,a co-simulation platform for a distributed drive electric vehicle was built based on nonlinear vehicle dynamics and tire force correction models.Thereafter,an adaptive fading unscented Kalman filter algorithm on road adhesion coefficient was proposed.In this algorithm,the road adhesion coefficient observers were designed for each wheel and the covariance matching criterion was subsequently applied to discriminate the divergence tre...