Sideslip angle estimation using extended Kalman filter

Abstract

Vehicle sideslip angle can be estimated using either dynamic or kinematic models. The dynamic model requires vehicle parameters, which might have uncertainties due to different load conditions, vehicle motions, and road frictions. Parameter uncertainties might result in estimation errors. Thus system identifications are required to estimate those parameters online. On the other hand, the kinematic model does not require these parameters. A closed-loop estimator can be formulated to estimate the sideslip angle using the kinematic model. Since the system matrix which consists of the yaw rate is time varying, the required input vector and output contain process and measurement noises, respectively, and the disturbance input matrix contains estimated states, extended Kalman filter is used to obtain the estimation gain in this paper. CarSim is used to evaluate the proposed approach under different driving scenarios and road frictions in Matlab/Simulink. The preliminary results show promising improvement of the sideslip angle estimation.