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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

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Autor(es):
Ribeiro, Alexandre M. [1] ; Moutinho, Alexandra [2] ; Fioravanti, Andre R. [1] ; de Paiva, Ely C. [3]
Número total de Autores: 4
Afiliação do(s) autor(es):
[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
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: Journal of the Brazilian Society of Mechanical Sciences and Engineering; v. 42, n. 1 JAN 2020.
Citações Web of Science: 0
Resumo

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)

Processo FAPESP: 18/04905-1 - Automatização de um Veículo Elétrico Robótico com Diferencial Eletrônico ("Auto_VERDE")
Beneficiário:André Ricardo Fioravanti
Modalidade de apoio: Auxílio à Pesquisa - Regular
Processo FAPESP: 18/05712-2 - Identificação e controle de um veículo elétrico robótico com diferencial eletrônico
Beneficiário:Alexandre Monteiro Ribeiro
Modalidade de apoio: Bolsas no Brasil - Doutorado