EKF软测量技术在汽车行驶状态估计中的应用

摘要

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.%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.