基于双容积卡尔曼滤波的车辆状态与路面附着系数估计  

Vehicle state and road friction coefficient estimation based on double cubature Kalman filter

作　　者：李刚[1,2] 解瑞春 卫绍元[1] 宗长富[2] 

机构地区：[1]辽宁工业大学汽车与交通工程学院,锦州121001 [2]吉林大学汽车仿真与控制国家重点实验室,长春130022

出　　处：《中国科学：技术科学》2015年第4期403-414,共12页Scientia Sinica(Technologica)

基　　金：国家自然科学基金青年基金(批准号:51305190);辽宁省教育厅项目(编号:L2013253);吉林大学汽车仿真与控制国家重点实验室开放基金(编号:20111104)资助项目

摘　　要：针对车辆行驶过程中的车辆状态与路面附着系数估计问题,论文研究了基于双容积卡尔曼滤波的车辆状态与路面附着系数估计算法,建立了采用Dugoff轮胎模型的三自由度车辆估算模型,基于双容积卡尔曼滤波理论设计了车辆行驶状态与路面附着系数估计器,使二者在估计过程中相互联系、形成闭环反馈,实现对车辆状态与路面附着的实时准确估计.选择典型工况,应用驾驶模拟器在环实验对双容积卡尔曼滤波估计算法进行了验证,并与双扩展卡尔曼滤波的估计算法进行对比分析.结果表明:基于双容积卡尔曼滤波的估计算法相对于基于双扩展卡尔曼滤波的估计算法更能够较准确地实现对车辆状态和路面附着系数的估计.For the estimation problem of vehicle state and road adhesion coefficient in driving, the estimation algorithm of vehicle state and road adhesion coefficient were studied in this paper based on double cubature Kalman filter. The 3-DOF nonlinear vehicle estimation model with Dugoff tire model was established. The vehicle driving state estimator and tire-road friction coefficient estimator based on double cubature Kalman filter were designed. The estimators contact with each other in the process of estimation and forms a closed loop feedback to estimate the vehicle state and road adhesion coefficient timely and accurately. Selecting the typical working condition, the algorithm was verified by driving simulator experiments in the loop. The results showed that estimation algorithm based on double cubature Kalman filter can more accurately estimate the vehicle state and road adhesion coefficient than estimation algorithm based on extend Kalman filter.

关 键 词：双容积卡尔曼滤波 车辆状态 路面附着 Dugoff轮胎 驾驶模拟器在环实验 

分 类 号：U461.51[机械工程—车辆工程]