Citation

Viehweger M, Vaseur C, van Aalst S, Acosta M, Regolin E, Alatorre A, Desmet W, Naets F, Ivanov V, Ferrara A, Victorino A. Veh. Syst. Dyn. 2021; 59(5): 675-702.

Copyright

(Copyright © 2021, Informa - Taylor and Francis Group)

DOI

10.1080/00423114.2020.1714672

PMID

unavailable

Abstract

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.

Language: en

Keywords

cog velocities; Kalman filtering; neural network; quaternion; sideslip angle; sliding mode observer; state estimation; tyre forces; Virtual sensing