An accurate estimation of a vehicle’s state of motion is the basis of dynamic stability control.Two different nonlinear Kalman filters are adopted for the estimation of the vehicle’s lateral/rollover stability state...An accurate estimation of a vehicle’s state of motion is the basis of dynamic stability control.Two different nonlinear Kalman filters are adopted for the estimation of the vehicle’s lateral/rollover stability state.First,the overall structure of the state estimation with four inputs and four outputs is introduced.After determining tire-cornering stiffness using a recursive leastsquares(RLS)method,the equations of state and of observation for the nonlinear Kalman filter are established based on a vehicle model with four degrees of freedom including planar and rollover dynamics.Then,the specific steps of real-time state estimation using the extended Kalman filter(EKF)and unscented Kalman filter(UKF)are both given.In a co-simulation,we find that the RLS algorithm estimates tire-cornering stiffness accurately and quickly,and the UKF improves the effect of state estimation compared with EKF.In addition,the UKF is verified against data from vehicle tests.The results show the proposed method is reliable and practical in estimating vehicle states.展开更多
基金the 111 Project(Grant No.B17034)National Natural Science Foundation of the People’s Republic of China(Grant No.51505354).
文摘An accurate estimation of a vehicle’s state of motion is the basis of dynamic stability control.Two different nonlinear Kalman filters are adopted for the estimation of the vehicle’s lateral/rollover stability state.First,the overall structure of the state estimation with four inputs and four outputs is introduced.After determining tire-cornering stiffness using a recursive leastsquares(RLS)method,the equations of state and of observation for the nonlinear Kalman filter are established based on a vehicle model with four degrees of freedom including planar and rollover dynamics.Then,the specific steps of real-time state estimation using the extended Kalman filter(EKF)and unscented Kalman filter(UKF)are both given.In a co-simulation,we find that the RLS algorithm estimates tire-cornering stiffness accurately and quickly,and the UKF improves the effect of state estimation compared with EKF.In addition,the UKF is verified against data from vehicle tests.The results show the proposed method is reliable and practical in estimating vehicle states.
