基于遗忘因子的UKF车辆状态参数估计算法  

Unscented Kalman filter for vehicle state parameter estimation based on forgetting factor

作　　者：冯亦奇 陈勇[1,2] FENG Yiqi;CHEN Yong(School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China;Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100192, China)

机构地区：[1]北京信息科技大学机电工程学院,北京100192 [2]北京电动车辆协同创新中心,北京100192

出　　处：《合肥工业大学学报（自然科学版）》2020年第11期1450-1455,1499,共7页Journal of Hefei University of Technology：Natural Science

基　　金：科技创新服务能力建设北京实验室建设新能源汽车北京实验室资助项目(PXM2019-014224-000005)。

摘　　要：针对车辆主动安全控制系统所需的传感器成本较高以及信号采集过程中易受到外界噪声干扰的问题,文章提出了一种利用遗忘因子概念对噪声协方差矩阵进行在线自适应调整的无迹卡尔曼滤波(unscented Kalman filter,UKF)方法,实现对行驶状态下的车辆状态参数进行实时在线估计。使用该方法对车辆横摆角速度、质心侧偏角、质心纵向速度、质心侧向速度4个状态参数进行了实时在线估计,并与CarSim软件的仿真结果进行了对比,验证该估计方法的有效性和准确性。研究结果表明,该文提出的参数估计算法对横摆角速度、质心侧偏角、质心侧向速度3个参数的估计精度比传统卡尔曼滤波方法分别提高了38%、27%、28%。Aiming at the problems of high sensor cost for vehicle active safety control system and system noise interference during signal acquisition,an adaptive unscented Kalman filter(UKF)method based on forgetting factor is proposed for real-time online estimation of vehicle driving state.The method estimates four parameters including the yaw angular velocity,sideslip angle,centroid longitudinal velocity and centroid lateral velocity of the vehicle.Finally,compared with the calculation results of CarSim software,the validity and accuracy of the estimation method are verified.The results show that the estimation accuracy of the proposed parameter estimation algorithm for the three parameters of yaw angular velocity,sideslip angle and centroid lateral velocity is 38%,27%and 28%higher than that of the traditional Kalman filter method respectively.

关 键 词：车辆状态 参数估计 遗忘因子 无迹卡尔曼滤波(UKF) 魔术公式 

分 类 号：U461.6[机械工程—车辆工程]