基于自适应卡尔曼滤波的汽车前后轴干扰力估计  

Vehicle Axles Disturbing Forces Estimation Based on Adaptive Kalman Filter

作　　者：林棻[1] 赵又群[1] 宋桂霞[1] 

机构地区：[1]南京航空航天大学车辆工程系,南京210016

出　　处：《系统仿真学报》2009年第5期1339-1342,共4页Journal of System Simulation

基　　金：高等学校博士学科点专项科研基金项目(20040287004)

摘　　要：针对汽车自动车道保持控制中汽车侧向干扰信息难以直接获取,提出把汽车动力学模型和自适应卡尔曼滤波理论相结合进行汽车前后轴干扰力估计。在线性二自由度汽车动力学模型中考虑前后轴的侧向力干扰,以辅助变量横摆角速度、侧向加速度和方向盘转角为量测信息,通过改进的自适应卡尔曼滤波算法建立了前后轴干扰力的最小方差估计,并对量测信号进行了滤波降噪。基于ADAMS/Car的虚拟试验验证了该算法具有较高的估计精度,可以为汽车侧向控制系统中估计器的设计提供理论指导。To aim at the problem that lateral disturbance on vehicle are too difficult to measure directly in automatic lane keeping, vehicle axle disturbing forces estimation algorithm was proposed based on vehicle dynamics model and adaptive Kalmanfilter theory. Linear vehicle model of two degree-of-freedom was used, in which lateral forces on front and rear axle were considered. Assistant variables of yaw rate, lateral acceleration and steering wheel angle were considered as measurement information. Linear minimum variance estimation of axle disturbing forces was obtained by improved adaptive Kalman filter recursive algorithm, and the measurement signals were denoised. The virtual experiment implemented by ADAMS/Car indicates that the algorithm has high accuracy. It can provide theoretic direction for design of estimator in vehicle lateral control system in lane keeping.

关 键 词：汽车前后轴干扰力 自适应卡尔曼滤波 动力学模型 状态估计 

分 类 号：U461.6[机械工程—车辆工程]