Estimation of sideslip angle and cornering stiffness of an articulated vehicle using a constrained lateral dynamics model

In this paper, a constrained lateral dynamics model of articulated vehicles and an algorithm for estimating sideslip angle and cornering stiffness are proposed. The articulated vehicle was modeled using the bicycle model, linear tire model, and modified Dug-off model. The normal force of each axle included in the model was estimated based on the longitudinal load transfer model. Physical constraints were applied to reduce model states. Accurate sideslip angle and cornering stiffness are essential for vehicle control safety and autonomous driving performance. The sideslip angle and cornering stiffness were simultaneously estimated using a dual linear time-varying (LTV) Kalman filter. The observability matrix guaranteed the convergence of the proposed estimation algorithm. The estimation performance was verified by simulation with TruckSim and an experiment using an articulated bus.

Automated summary

This paper proposes a constrained lateral dynamics model of articulated vehicles and an algorithm for estimating sideslip angle and cornering stiffness. The articulated vehicle is modeled using the bicycle model, linear tire model, and modified Dug-off model, with the estimation of normal force on each axle based on the longitudinal load transfer model. Accurate sideslip angle and cornering stiffness are crucial for vehicle control safety and autonomous driving performance. The dual linear time-varying Kalman filter is used to simultaneously estimate the sideslip angle and cornering stiffness, with the observability matrix ensuring the convergence of the estimation algorithm. The estimation performance is verified through simulation and experimental validation.