摘要
整车质量与质心位置是汽车重要的结构参数,也是试验中必要的量测值和主动安全控制系统必需的工作参数.文中针对汽车实际使用过程中质量经常发生变化的情况,提出一种汽车双参数联合辨识方法.该方法基于两个串行的递推最小二乘(RLS)法,以汽车出厂初始参数为串行RLS辨识算法的初始值,结合蛇行试验辨识质心位置;然后,以辨识所得的质心位置结合双移线试验辨识整车质量,将辨识所得质量序列方差作为门槛值,通过有限次的递推循环,可以使得汽车整车质量、质心至前轴距离两参数的相对误差均收敛到3%以内.文中最后通过ADAMS虚拟试验验证了算法的有效性.
Gross mass and centroid position are two important structural parameters of vehicles that are necessary to be measured in experiments and are essential to the proper operation of vehicle＇s active safety control system. As the gross mass frequently changes in practice, a dual-parameter combined identification method for vehicles is proposed. This method, which is based on two serial recursive least squares （RLS） procedures, uses the original vehicle parameters as the initial parameters of the serial RLS-based identification algorithm and identifies the eentroid position with the combination of the pylon course slalom test. Then, the identified centroid position is applied to the identification of vehicle mass with the combination of the double-lane change test. Moreover, by taking the variance of the identified vehicle mass sequence as the threshold and by performing limited recursive circulation, the relative errors of the vehicle mass and the distance from the centroid to the front axle both converge to less than 3 %. The effectiveness of the proposed algorithm is finally verified by the virtual test in ADAMS.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2012年第12期105-110,共6页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(10902049)
中国博士后科学基金资助项目(2012M521073)
关键词
车辆工程
参数辨识
递推最小二乘法
串行
vehicle engineering
parameter identification
recursive least squares method
serial
