Article Dans Une Revue Vehicle System Dynamics Année : 2020

Vehicle state and tyre force estimation: demonstrations and guidelines

Résumé

This paper presents an in-depth analysis of the application of different techniques for vehicle state and tyre force estimation using the same experimental data and vehicle models, except for the tyre models. Four schemes are demonstrated: (i) an Extended Kalman Filter (EKF) scheme using a linear tyre model with stochastically adapted cornering stiffness, (ii) an EKF scheme using a Neural Network (NN) data-driven linear tyre model, (iii) a tyre model-less Suboptimal-Second Order Sliding Mode (S-SOSM) scheme, and (iv) a Kinematic Model (KM) scheme integrated in an EKF. The estimation accuracy of each method is discussed. Moreover, guidelines for each method provide potential users with valuable insight into key properties and points of attention.

Dates et versions

hal-03135009 , version 1 (08-02-2021)

Identifiants

Citer

Marco Viehweger, Cyrano Vaseur, Sebastiaan van Aalst, Manuel Acosta, Enrico Regolin, et al.. Vehicle state and tyre force estimation: demonstrations and guidelines. Vehicle System Dynamics, 2020, 59 (5), pp.675-702. ⟨10.1080/00423114.2020.1714672⟩. ⟨hal-03135009⟩
113 Consultations
0 Téléchargements

Altmetric

Partager

  • More