Sideslip Angle Estimation with Variable Cornering Stiffness for Modular Transporter

A modular transporter is a heavy-duty vehicle designed for on-site transport of high loads, where accurate estimation of sideslip angle plays a crucial role in maintaining motion stability and transport safety. However, existing studies on sideslip angle estimation have primarily focused on conventional vehicles, with limited attention given to the modular transporter. To address this issue, this paper proposes a novel filtering framework for estimating sideslip angle while considering variable cornering stiffness for the modular transporter. The framework integrates a novel robust adaptive cubature Kalman filter (RACKF) with regularized recursive least squares (RRLS), collectively referred to as RACKF-RRLS. In the RACKF, a Huber estimation strategy is employed as the robust component to eliminate large outliers, while an H-infinity technique serves as the adaptive component to enhance filtering accuracy. The superiority of the RACKF-RRLS is validated through comparisons with ten baseline estimators. A full-scale vehicle experiment was conducted under unloaded and 120 t load conditions, with both high- and low-speed scenarios considered for the 120 t case. Based on these experiments, a total of 66 estimation cases were analyzed. The results show that the RACKF-RRLS outperforms all baseline estimators across different scenarios, with its average RMSE being 72.727%, 56.364%, 70.309%, and 39.496% lower than those of the CKF, RCKF, ACKF, and RACKF, respectively. These findings demonstrate the potential for developing an advanced sideslip angle estimator, offering benefits for vehicle technology.