Abstract
To address the problem of poor robustness and accuracy of vehicle state and parameter estimation by conventional Kalman filter in the non-Gaussian environments, a three-degree-of-freedom vehicle model with an improved Dugoff tire model is established and a joint estimator of vehicle state and parameter is designed using the Maximum Correntropy (MC) adaptive unscented Kalman filter (AUKF) algorithm in order to simultaneously estimate and identify the yaw rate, longitudinal vehicle speed, lateral vehicle speed, vehicle mass and rotational inertia. The proposed joint estimator algorithm was validated by Simulink/CarSim simulation testbed under Double Lane Change and Sine Wave Steering Input conditions. The results show that MC combined with AUKF (MCAUKF) algorithm has higher estimation accuracy and better convergence compared to the unscented Kalman filter (UKF) and the MC combined with UKF (MCUKF) in non-Gaussian environments, and the MCAUKF estimator is more suitable for state estimation and parameter identification of real vehicles.
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This work was supported by the National Natural Science Foundation of China (grant numbers 61663042).
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Zhang, F., Feng, J., Qi, D. et al. Joint Estimation of Vehicle State and Parameter Based on Maximum Correntropy Adaptive Unscented Kalman Filter. Int.J Automot. Technol. 24, 1553–1566 (2023). https://doi.org/10.1007/s12239-023-0125-3
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DOI: https://doi.org/10.1007/s12239-023-0125-3