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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 11/2018

13.03.2017 | Original Article

Tire lateral force estimation and grip potential identification using Neural Networks, Extended Kalman Filter, and Recursive Least Squares

verfasst von: Manuel Acosta, Stratis Kanarachos

Erschienen in: Neural Computing and Applications | Ausgabe 11/2018

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Abstract

This paper presents a novel hybrid observer structure to estimate the lateral tire forces and road grip potential without using any tire–road friction model. The observer consists of an Extended Kalman Filter structure, which incorporates the available prior knowledge about the vehicle dynamics, a feedforward Neural Network structure, which is used to estimate the highly nonlinear tire behavior, and a Recursive Least Squares block, which predicts the road grip potential. The proposed observer was evaluated under a wide range of aggressive maneuvers and different road grip conditions using a validated vehicle model, validated tire model, and sensor models in the simulation environment IPG CarMaker ® . The results confirm its good and robust performance.
Vorheriger Artikel Coupling of optimal variation of parameters method with Adomian’s polynomials for nonlinear equations representing fluid flow in different geometries
Nächster Artikel Numerical study of radiative Maxwell viscoelastic magnetized flow from a stretching permeable sheet with the Cattaneo–Christov heat flux model

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Metadaten
Titel
Tire lateral force estimation and grip potential identification using Neural Networks, Extended Kalman Filter, and Recursive Least Squares
verfasst von
Manuel Acosta
Stratis Kanarachos
Publikationsdatum
13.03.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 11/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-2932-9

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