Intelligent vehicle sideslip angle estimation considering measurement signals delay

Considering vehicle sideslip angle estimation difficulty under severe driving conditions with dynamic model based method due to vehicle nonlinear characteristic and parameter uncertainty, a novel kinematic model based method is proposed with the fusion of intelligent vehicle sensors. The state space models of the vehicle yaw angle and the roll angle are constructed based on the IMU and the lateral arrangement of the dual-GPS. In order to reduce the weight of the previously estimated value, an adaptive fading Kalman filtering algorithm is adopted to improve the filtering effect on the yaw and roll angle. A nonlinear adaptive observer is constructed to estimate the vehicle sideslip angle with the integration of the road line information from the camera, velocity from the GPS and acceleration/ angular velocity from the IMU. Furthermore, compared with the IMU, the information obtained from the GPS and the camera can't be utilized directly as large measurement delay. Thus, an observer-predictor is developed with multi-sensor fusion to handle the measurement delay problem. Finally, the proposed algorithm is validated through co-simulation under different maneuvers.