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

2026-02-10

AI Summary

This work proposes a model-based output-feedback lateral control strategy to address the challenges posed by inaccurate tire dynamics modeling in conventional vehicle control, particularly the low-speed micro-shimmy that degrades path-tracking accuracy and robustness. By integrating a distributed-parameter tire dynamics model governed by partial differential equations (PDEs) with intelligent tire sensing technology, the authors formulate a coupled ordinary differential equation–partial differential equation (ODE-PDE) system. A dedicated state observer and a force-based tracking controller are co-designed to jointly estimate key variables—including tire sideslip angle, lateral force, and vehicle motion states—and actively suppress micro-shimmy at low speeds. Experimental validation demonstrates that the proposed approach achieves high-precision, robust all-wheel steering path tracking across diverse driving conditions.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• lateral tracking control
• all-wheel steering vehicles
• smart tires
• tire dynamics
• micro-shimmy

Innovation

• model-based control
• smart tires
• ODE-PDE systems
• lateral tracking
• distributed tire dynamics