Vehicle Lateral Velocity and Lateral Tire-road Forces Estimation Based on Switched Interval Observers

Ifqir, Sara; Ichalal, Dalil; Ait-Oufroukh, Naima; Mammar, Said · 2020 · Crossref

DOI: 10.23919/acc45564.2020.9147793

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Summary

This paper addresses the challenge of estimating vehicle lateral velocity and lateral tire-road forces, critical parameters for vehicle stability and safety systems that are difficult to measure directly due to technical and economic constraints. Existing estimation methods often rely on deterministic analyses assuming accurate knowledge of vehicle parameters, which is unrealistic given uncertainties in mass, center of gravity, and particularly tire cornering stiffness, which varies with road conditions. The authors propose a novel framework using switched interval observers to provide guaranteed bounds for these states, accounting for parameter uncertainties and changes in tire operating conditions. The methodology employs a two-degree-of-freedom bicycle model for lateral dynamics, incorporating a first-order transient tire model to capture dynamic tire behavior rather than relying on static models. To handle the time-varying nature of longitudinal velocity and uncertain cornering stiffness, the vehicle dynamics are represented as a switched uncertain linear system, where subsystems correspond to different longitudinal velocity ranges. The cornering stiffness is treated as an unknown but bounded parameter. The authors design a switched interval observer that calculates upper and lower bounds for the state vector. The existence conditions for this observer are formulated as an optimization problem using Linear Matrix Inequalities (LMIs), ensuring Input-to-State Stability and tight interval estimation errors. The proposed algorithm is evaluated through simulations and validated against experimental data acquired from an instrumented vehicle. The results demonstrate that the switched interval observer reliably estimates the upper and lower bounds of vehicle lateral velocity and lateral tire forces during both steady and transient maneuvers. The estimated intervals remain consistent with measurements, the vehicle model, and the bounded uncertainties, effectively handling the variations in tire-road interaction. The significance of this work lies in its application of guaranteed interval estimation to safety-critical automotive applications, a field where such methods were previously underutilized. By requiring only the bounds of tire-road parameters rather than precise values, the approach offers a robust solution for virtual sensing in autonomous and assisted driving systems. The inclusion of dynamic tire models and the use of real experimental data further distinguish this study from prior works that often rely on static models and purely theoretical validations. This framework provides a reliable tool for monitoring vehicle lateral dynamics under uncertain and varying conditions, enhancing the performance of active safety systems.

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