Estimation of Tire Forces and Torques Via Nonlinear Suspension Models and Optimal Control
Abstract
Accurately determining the forces and moments exchanged between a tire and the road is essential for vehicle safety, performance, and comfort. However, direct measurement is often impractical, as tire-road interactions heavily depend on road surface conditions, tire rubber properties, and the complex dynamics of the suspension. Conventional wheel force transducers are typically expensive, while strain gauges offer a low-cost and easy-to-integrate alternative. The key challenge, however, lies in transforming strain signals into reliable estimates of tire and suspension loads.
This paper presents a methodology to estimate the complete set of tire forces and moments, along with internal suspension reactions, using only strain gauge data. The approach relies on a nonlinear suspension model that incorporates asymmetric tension-compression behavior to accurately capture system static and dynamic characteristics. To handle real-world issues such as faulty or missing sensor data, the tire forces and moments estimation problem is formulated as an optimal control problem, enabling reconstruction of tire loads from strain measurements while respecting vehicle dynamical constraints.
The method is embedded in a symbolic modeling framework that automatically generates all required models for simulation and control. This ensures consistency, reduces manual modeling effort, and allows straightforward adaptation to different suspension configurations, demonstrating strong potential for both research and industrial applications.