Lateral tracking control of all-wheel steering vehicles with intelligent tires

The accurate characterization of tire dynamics is critical for advancing control strategies in autonomous road vehicles, as tire behavior significantly influences handling and stability through the generation of forces and moments at the tire-road interface. Smart tire technologies have emerged as a promising tool for sensing key variables such as road friction, tire pressure, and wear states, and for estimating kinematic and dynamic states like vehicle speed and tire forces. However, most existing estimation and control algorithms rely on empirical correlations or machine learning approaches, which require extensive calibration and can be sensitive to variations in operating conditions. In contrast, model-based techniques, which leverage infinite-dimensional representations of tire dynamics using partial differential equations (PDEs), offer a more robust approach. This paper proposes a novel model-based, output-feedback lateral tracking control strategy for all-wheel steering vehicles that integrates distributed tire dynamics with smart tire technologies. The primary contributions include the suppression of micro-shimmy phenomena at low speeds and path-following via force control, achieved through the estimation of tire slip angles, vehicle kinematics, and lateral tire forces. The proposed controller and observer are based on formulations using ODE-PDE systems, representing rigid body dynamics and distributed tire behavior. This work marks the first rigorous control strategy for vehicular systems equipped with distributed tire representations in conjunction with smart tire technologies.

💡 Research Summary

The paper addresses a critical gap in autonomous vehicle control by developing a model‑based, output‑feedback lateral tracking controller that explicitly incorporates distributed tire dynamics and intelligent‑tire sensing. Traditional estimation and control schemes for tire forces and slip angles rely heavily on empirical correlations or machine‑learning models, which demand extensive calibration and are sensitive to variations in road conditions, temperature, wear, and tire pressure. To overcome these limitations, the authors adopt an infinite‑dimensional representation of the tire using partial differential equations (PDEs) that capture the transport‑like deformation of the rubber within the contact patch and the first‑order compliance of the carcass.

The vehicle is modeled as a linear single‑track (bicycle) system with all‑wheel steering. The rigid‑body dynamics are expressed by two ordinary differential equations (ODEs) for lateral velocity and yaw rate, while each axle’s tire is described by a transport PDE defined on the normalized spatial domain ξ∈