摘要
作为车辆操纵稳定性的关键评价指标及车辆稳定性控制目标,准确估计车辆的质心侧偏角直接影响车辆的行驶安全性,为此提出了一种基于自适应奇异值分解无迹卡尔曼滤波算法的车辆质心侧偏角估计方法,以改善无迹卡尔曼滤波(Unscented Kalman Filter,UKF)在系统强非线性或状态模型不精确的情况下状态估计精度降低甚至发散的问题。利用自适应奇异值分解方法(Singular Value Decomposition,SVD)构建sigma点,并在时间更新过程中利用自适应因子对奇异矩阵进行修正,改进了UKF中状态协方差矩阵的迭代稳定性及估计器的鲁棒性。利用分布式驱动电动汽车半实物仿真平台分别在双移线工况、单移线工况及方向盘转角阶跃工况下对基于无迹卡尔曼滤波和ASVD-UKF算法的质心侧偏角估计方法进行了对比验证。结果表明,ASVD-UKF估计器改善了无迹卡尔曼滤波估计器的精度,验证了算法的有效性。
As the key evaluation indicator of vehicle handling stability and the control target of vehicle stability,accurate estimation of sideslip angle of vehicle centroid directly affects the driving safety of the vehicle.To this end,a vehicle centroid sideslip angle estimation method based on ASVD UKF algorithm is proposed to improve the reduction or even divergent of the state estimation accuracy of the UKF when the system is strongly nonlinear or the state model is inaccurate.The sigma point is constructed by using ASVD method,and the singular matrix is modified by using adaptive factor during the time update process which improved the iterative stability of the state covariance matrix in UKF and the robustness of the estimator.The centroid sideslip angle estimation methods based on UKF and ASVD-UKF algorithm are comparatively verified by using the semi-physical simulation platform of the distributed drive electric vehicle under the working conditions of double-line shifting,single-line shifting and steering wheel angle step respectively.The result shows that the ASVD-UKF estimator improved the accuracy of UKF estimator and verified the effectiveness of the algorithm.
作者
王姝
赵轩
余强
WANG Shu;ZHAO Xuan;YU Qiang(School of Automobile,Chang'an University,Xi'an Shaanxi 710064,China)
出处
《公路交通科技》
CAS
CSCD
北大核心
2020年第12期133-141,共9页
Journal of Highway and Transportation Research and Development
关键词
汽车工程
侧偏角估计
自适应奇异值分解
无迹卡尔曼滤波
automobile engineering
sideslip angle estimation
adaptive singular value decomposition(ASVD)
unscented Kalman filtering(UKF)
