Advanced search
Start date
Betweenand
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Estimation of tire-road friction for road vehicles: a time delay neural network approach

Full text
Author(s):
Ribeiro, Alexandre M. [1] ; Moutinho, Alexandra [2] ; Fioravanti, Andre R. [1] ; de Paiva, Ely C. [3]
Total Authors: 4
Affiliation:
[1] Sch Mech Engn, Dept Computat Mech, Mendeleyev St 200, BR-13083860 Campinas, SP - Brazil
[2] Univ Lisbon, Inst Super Tecn, LAETA, IDMEC, Inst Mech Engn, Av Rovisco Pais, P-1049001 Lisbon - Portugal
[3] Sch Mech Engn, Dept Integrated Syst, Mendeleyev St 200, BR-13083860 Campinas, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: Journal of the Brazilian Society of Mechanical Sciences and Engineering; v. 42, n. 1 JAN 2020.
Web of Science Citations: 0
Abstract

The performance of vehicle active safety systems is dependent on the friction force arising from the contact of tires and the road surface. Therefore, an adequate knowledge of the tire-road friction coefficient is of great importance to achieve a good performance of different vehicle control systems. This paper deals with the tire-road friction coefficient estimation problem through the knowledge of lateral tire force. A time delay neural network (TDNN) is adopted for the proposed estimation design. The TDNN aims at detecting road friction coefficient under lateral force excitations avoiding the use of standard mathematical tire models, which may provide a more efficient method with robust results. Moreover, the approach is able to estimate the road friction at each wheel independently, instead of using lumped axle models simplifications. Simulations based on a realistic vehicle model are carried out on different road surfaces and driving maneuvers to verify the effectiveness of the proposed estimation method. The results are compared with a classical approach, a model-based method modeled as a nonlinear regression. (AU)

FAPESP's process: 18/04905-1 - Automatization of a Robotic Electric Vehicle with Electronic Differential
Grantee:André Ricardo Fioravanti
Support type: Regular Research Grants
FAPESP's process: 18/05712-2 - Identification and control of a robotic electric vehicle with electronic differential
Grantee:Alexandre Monteiro Ribeiro
Support type: Scholarships in Brazil - Doctorate