Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/25987
Author(s): Ricardo de Castro
Rui Esteves Araújo
Jaime S. Cardoso
Diamantino Freitas
Title: A new linear parametrization for peak friction coefficient estimation in real time
Issue Date: 2010
Abstract: The correct estimation of the friction coefficient in automotive applications is of paramount importance in the design of effective vehicle safety systems. In this article a new parametrization for estimating the peak friction coefficient, in the tire-road interface, is presented. The proposed parametrization is based on a feedforward neural network (FFNN), trained by the Extreme Learning Machine (ELM) method. Unlike traditional learning techniques for FFNN, typically based on backpropagation and inappropriate for real time implementation, the ELM provides a learning process based on random assignment in the weights between input and the hidden layer. With this approach, the network training becomes much faster, and the unknown parameters can be identified through simple and robust regression methods, such as the Recursive Least Squares. Simulation results, obtained with the CarSim program, demonstrate a good performance of the proposed parametrization; compared with previous methods described in the literature, the proposed method reduces the estimation errors using a model with a lower number of parameters.
Subject: Engenharia de controlo, Veículos eléctricos, Engenharia electrotécnica, electrónica e informática
Control engineering, Electric vehicles, Electrical engineering, Electronic engineering, Information engineering
Scientific areas: Ciências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática
Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
URI: https://repositorio-aberto.up.pt/handle/10216/25987
Source: Proceedings of the IEEE Vehicle Power and Propulsion Conference - VPPC 2010
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: openAccess
License: https://creativecommons.org/licenses/by-nc/4.0/
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

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