A Robust Adaptive Estimator for Sideslip Angle and Tire-Road Forces Under Time-Varying and Abnormal Noise

A critical challenge in vehicle state estimation is managing unknown and non-Gaussian noise characteristics resulting from sensor degradation, measurement abnormalities, model inaccuracies, and external interference. However, existing research has problems with filter divergence and insufficient accuracy and robustness. To this end, we propose a robust adaptive filtering estimator, the variational Bayesian maximum correntropy square-root cubature Kalman filter (VBMCSCKF), for the joint estimation of vehicle sideslip angle and tire-road longitudinal/lateral forces. The proposed VBMCSCKF utilizes onboard inertial measurement unit (IMU) data to adaptively estimate noise covariance matrices via the variational Bayesian (VB) method and introduces the maximum correntropy criterion (MCC) for further correction to enhance its accuracy and robustness under unknown and abnormal noise. These features are embedded within the square-root cubature Kalman filter (SCKF) framework, and the estimator for tire force and sideslip angle is developed based on a zero-first derivative and vehicle model. Numerical simulations and Monte Carlo simulation of a double-lane change maneuver under varying speeds and road adhesion conditions demonstrate impressive estimation results for tire-road force and the vehicle sideslip angle. It effectively adapts to time-varying IMU noise and resists abnormal IMU measurements. Compared to MCSCKF and VBSCKF, the performance of VBMCSCKF improves by 44.0% and 28.5%, respectively. Notably, the increase in computation time for VBMCSCKF compared to the standard SCKF is almost negligible.