摘要
准确实时获取行驶过程中的状态信息是汽车动态控制系统研究的关键问题。将unscented卡尔曼滤波(UKF)算法应用到汽车的状态估计之中,建立了包含时不变统计特性噪声和非线性轮胎的汽车动力学模型,采用具有对称采样策略和比例修正的UKF算法对汽车估计了多个关键状态量。将UKF估计器与常见的EKF估计器进行了比较分析,基于ADAMS/Car的虚拟试验和实车试验验证了UKF在汽车状态估计中的可行性。
A critical component of vehicle dynamic control systems is as accurate and real time knowledge of vehicle key states when running on road.UKF algorithm was used in vehicle state estimation.The nonlinear vehicle dynamics system which contained constant noise and nonlinear tire model was established.Several vehicle key states were estimated using UKF with symmetrical sampling strategy and proportional correction.The estimator based on UKF is compared with the estimator based on extended Kalman filter （EKF）.The results of virtual experiments based on ADAMS/Car and real vehicle experiments demonstrate that UKF is available in vehicle state estimation.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2010年第5期615-619,629,共6页
China Mechanical Engineering
基金
国家自然科学基金资助项目(10902049)
国家863高技术研究发展计划资助项目(2008AA11A140)
