Abstract: Side slip angle and yaw rate are the important control parameters of vehicle stability control system, and getting accurate state information of driving process is the key issue of control system research. A common estimation method based on the estimation theory is that using sensors to get easily measured variables, and then estimating the key state variables which are difficult to measure. A new particle filtering algorithm is proposed to estimate vehicle key states with a 7-DOF nonlinear vehicle dynamic model which contained constant noise and nonlinear tire model. For particle degradation during particle filtering process, the iterative extended Kalman filtering algorithm is used to produce importance density function which is more close to the true state, and auxiliary particle filtering algorithm with the latest observation information is used to resample particle with the observation. The iterative extended Kalman filtering-auxiliary particle filtering algorithm(IEKF-APF) combines of the above two algorithms to improve the particle resampling and estimation precision. To validate the estimation performance of IEKF-APF, compare the estimation results of IEKF-APF simultaneously with road test values and unscented Kalman filtering algorithm(UKF) estimation results, and the comparison shows that IEKF-APF estimation performance is better than that of UKF, and its estimation results are closer to the test results.

Key words: particle filtering, side slip angle, state estimation, vehicle dynamic, yaw rate