Inverse-dynamics observer design for a linear single-track vehicle model with distributed tire dynamics

TL;DR

This paper introduces an innovative observer for linear single-track vehicle models that accurately estimates sideslip angles and tire forces using distributed tire dynamics and standard sensor data, improving safety and handling.

Contribution

It presents a novel observer design combining PDE-based tire modeling with dynamical inversion for state estimation from limited sensor data.

Findings

Effective estimation of sideslip angle and tire forces demonstrated in simulations.

Robustness to noise and model uncertainties confirmed.

Improved vehicle safety and handling potential.

Abstract

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.