Robust estimation of sideslip angle for heavy-duty vehicles under payload conditions using a series-connected structure estimator

The robust estimation of heavy-duty vehicles (HDVs) sideslip angle is crucial for vehicle stability controls. This paper aims to address the challenge of estimating the sideslip angle for HDVs when carrying cargo. We propose an innovative framework for estimating the sideslip angle of HDVs after loading. To address the problem of deviation in the inertial measurement unit (IMU) output acceleration signal during vehicle turning caused by the change of center of gravity (C.G.) position after loading cargo, we amalgamate the minimum model error (MME) criterion with the cubature Kalman filter (CKF) to accurately estimate the longitudinal C.G. position and carry out the IMU sensor position correction. On this basis, concerning the estimation of the sideslip angle for HDV when carrying cargo, a series-connected structure estimator is proposed, comprising a kinematic model estimator, a tire cornering stiffness estimator, and a dynamics model estimator. In this structure, the kinematic and dynamics model estimator employs CKF. Considering the characteristics of both kinematic and dynamics models, alongside HDVs' variable payload, an innovative adaptive weighted recursive least squares (AWRLS) method is designed for the adaptive estimation of tire cornering stiffness. This allows the series-connected structure estimator to dynamically adjust in real-time, achieving an accurate and robust estimation of the sideslip angle. Finally, through extensive simulations and real-world vehicle experiments, we validate the efficacy of the proposed framework. The results of the experiments show that the proposed method has a satisfactory level of estimation accuracy, significantly outperforming the estimation results of individual models.