Study on vehicle state and tire–road friction coefficient estimation based on maximum correntropy generalized high-degree cubature Kalman filter

Abstract

To cope with the challenges of inadequate precision and weak robustness of tire–road friction coefficient (TRFC) and vehicle state estimation under mixed Gaussian noise circumstances, this research puts forward a method combining maximum correntropy criterion (MCC) and generalized high-degree cubature Kalman filter (GHCKF). A coupled lateral and longitudinal vehicle model and the Dugoff tire model are established. The vehicle mass is estimated using the recursive least squares approach based on a forgetting factor (FFRLS). Low-cost onboard sensors are utilized to design observers for vehicle state and TRFC. The observers’ effectiveness is tested by Carsim/Simulink co-simulation tests under the conditions of sine steering input and steering wheel angle step input. The research results indicate that in handling mixed Gaussian noise environments, maximum correntropy generalized high-degree cubature Kalman filter (MCGHCKF) method outperforms maximum correntropy cubature Kalman filter (MCCKF), GHCKF, and cubature Kalman filter (CKF) methods concerning robustness and estimation accuracy, providing stable and reliable support for systems of vehicle active safety control.