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Authors: Manuel Acosta ; Stratis Kanarachos and Michael E. Fitzpatrick

Affiliation: Coventry University, United Kingdom

ISBN: 978-989-758-263-9

Keyword(s): Virtual Sensors, Tire Force Estimation, Unscented Kalman Filter, Adaptive Kalman Filter, Neural Networks.

Related Ontology Subjects/Areas/Topics: Adaptive Signal Processing and Control ; Informatics in Control, Automation and Robotics ; Nonlinear Signals and Systems ; Signal Processing, Sensors, Systems Modeling and Control ; System Modeling

Abstract: In this paper, a novel approach to estimate the longitudinal, lateral and vertical tire forces is presented. The innovation lies a) in the proposition of a modular state estimation architecture that lessens the tuning effort and ensures the filter’s stability and b) in the estimation of the longitudinal velocity relying only on the wheel speed information.The longitudinal forces are estimated using an Adaptive Random-Walk Linear Kalman Filter. The lateral forces per axle are estimated by combining an Adaptive Unscented Kalman filter and Neural Networks. The individual tire lateral forces are inferred from the axle lateral forces using the vertical load proportionality principle. The individual tire vertical forces are estimated using a steady-state weight transfer approach, in which the roll stiffness distribution is considered. The state estimator is implemented in Simulink R and simulations are carried out in the vehicle dynamics simulation software IPG CarMaker R . The v irtual sensor is tested in aggressive and steady-state maneuvers, exhibiting in both cases a remarkable performance. (More)

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Paper citation in several formats:
Acosta, M.; Kanarachos, S. and Fitzpatrick, M. (2017). A Virtual Sensor for Integral Tire Force Estimation using Tire Model-less Approaches and Adaptive Unscented Kalman Filter.In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-263-9, pages 386-397. DOI: 10.5220/0006394103860397

@conference{icinco17,
author={Manuel Acosta. and Stratis Kanarachos. and Michael E. Fitzpatrick.},
title={A Virtual Sensor for Integral Tire Force Estimation using Tire Model-less Approaches and Adaptive Unscented Kalman Filter},
booktitle={Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2017},
pages={386-397},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006394103860397},
isbn={978-989-758-263-9},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - A Virtual Sensor for Integral Tire Force Estimation using Tire Model-less Approaches and Adaptive Unscented Kalman Filter
SN - 978-989-758-263-9
AU - Acosta, M.
AU - Kanarachos, S.
AU - Fitzpatrick, M.
PY - 2017
SP - 386
EP - 397
DO - 10.5220/0006394103860397

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