摘要
采用一种基于EKF搭建的软测量算法,对汽车纵向车速、质心侧偏角和横摆角速度动态参数进行估计.建立了估算用的3自由度非线性车辆数学模型,EKF利用低成本传感器测得的纵向加速度、侧向加速度和方向盘转角信号,有效地实现对汽车行驶状态进行较为精确的估计.最后通过Carsim与Matlab/Simulink联合仿真对EKF算法进行了验证,从而证实了EKF软测量技术能够准确、实时地估计汽车动态参数.
Extended kalman filter soft computing algorithm is proposed and applied to estimate longitudinal velocity,slip angle and yaw rate of vehicle running in this paper.A non-linear estimation model is established for three degrees of freedom vehicle and the extended Kalman filter applies low-cost sensor signals including the longitudinal acceleration,lateral acceleration and steering wheel angle in order to achieve the accurate estimates of the vehicle states.Finally co-simulation is carried out based on Carsim and Matlab/Simulink.The results prove that EKF can accurately and real-time estimate the dynamic vehicle parameters.
作者
郝亮
李刚
刘树伟
HAO Liang , LI Gang, LIU Shuwei(School of Automobile and Traffic Engineering, Liaoning University of Technology,Jinzhou 121001, Liaoning, Chin)
出处
《中国工程机械学报》
北大核心
2017年第5期466-470,共5页
Chinese Journal of Construction Machinery
基金
辽宁省教育厅重大科技平台资助项目(JP2016011)
关键词
EKF
软测量算法
动态参数
精确估计
联合仿真
extended kalman filter
soft computing algorithm
dynamic parameters
accurate estimates
co-simulation
