Inverse-dynamics observer design for a linear single-track vehicle model with distributed tire dynamics
This work addresses the problem of accurately estimating vehicle sideslip angle and tire forces for enhancing safety and handling performances in unknown driving scenarios, representing an incremental improvement in vehicle state estimation.
This paper proposes an observer that estimates a vehicle's sideslip angle and tire forces using a linear single-track model combined with a distributed representation of tires. The observer reconstructs lumped and distributed vehicle states from yaw rate and lateral acceleration measurements, demonstrating effectiveness in simulations even with noise and model uncertainties.
Accurate estimation of the vehicle's sideslip angle and tire forces is essential for enhancing safety and handling performances in unknown driving scenarios. To this end, the present paper proposes an innovative observer that combines a linear single-track model with a distributed representation of the tires and information collected from standard sensors. In particular, by adopting a comprehensive representation of the tires in terms of hyperbolic partial differential equations (PDEs), the proposed estimation strategy exploits dynamical inversion to reconstruct the lumped and distributed vehicle states solely from yaw rate and lateral acceleration measurements. Simulation results demonstrate the effectiveness of the observer in estimating the sideslip angle and tire forces even in the presence of noise and model uncertainties.