摘要
为了改善复杂系统中单采样率控制策略的控制品质,在深入研究多采样率数字控制系统和卡尔曼滤波算法的基础上,提出了输入多采样率的卡尔曼滤波算法.将该算法应用于汽车主动前轮转向系统中,对横摆角速度、质心侧偏角和纵向车速进行估计,通过Carsim与Simulink的联合仿真以及蒙特卡罗试验,验证了算法的有效性.研究结果表明:多采样率卡尔曼滤波算法有利于融合多个输入量的信息,能改善状态估计器的性能,比单采样率卡尔曼滤波算法具有更高的稳定性,且估计误差减小4.0%～48.7%.
In order to improve the control quality of the single-rate digital control strategy in a complex system,an input multirate unscented Kalman filter（IMRUKF） was developed by combination of multirate digital control systems and the unscented Kalman filter（UKF） algorithm.The IMRUKF algorithm was applied to estimate the yaw rate,slip angle,and longitudinal velocity of the active front steering（AFS） system of vehicles.Then,a co-simulation and a Monte Carlo experiment were carried out using Carsim and Simulink.The results show that IMRUKF improves the ability of the state estimator by integrating multi-input information.Compared with the single-rate UKF algorithm,the IMRUKF algorithm has a higher stability and a smaller estimation error（with a reduction in the error of around 4.0% to 48.7%）.
出处
《西南交通大学学报》
EI
CSCD
北大核心
2012年第5期849-854,894,共7页
Journal of Southwest Jiaotong University
基金
国家自然科学基金资助项目(51177137
61134001)
中央高校基本科研业务费专项资金资助项目(SWJTU11CX034)
关键词
多采样率数字控制系统
无迹卡尔曼滤波
状态估计
主动前轮转向
multirate digital control system
unscented Kalman filter（UKF）
state estimation
active front steering（AFS）
