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Neural Computing and Applications
Published 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

Authors: Manuel Acosta, Stratis Kanarachos

Published in: Neural Computing and Applications | Issue 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.
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Metadata
Title
Tire lateral force estimation and grip potential identification using Neural Networks, Extended Kalman Filter, and Recursive Least Squares
Authors
Manuel Acosta
Stratis Kanarachos
Publication date
13-03-2017
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 11/2018
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-2932-9

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