A Virtual Sensor for Integral Tire Force Estimation using Tire Model-Less Approaches and Adaptive Unscented Kalman Filter

Manuel Acosta, Stratis Kanarachos, Michael E. Fitzpatrick

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding

9 Citations (Scopus)

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 virtual sensor is tested in aggressive and steady-state maneuvers, exhibiting in both cases a remarkable performance.
Original languageEnglish
Title of host publicationICINCO 2017 - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics
PublisherSciTePress
Pages386-397
Number of pages12
Volume1
ISBN (Electronic)9789897582639
DOIs
Publication statusPublished - 26 Jul 2017
Event14th International Conference on Informatics in Control, Automation and Robotics - Madrid, Spain
Duration: 26 Jul 2017 → 28 Jul 2017
http://www.ieee-ras.org/component/rseventspro/event/1040-icinco-2017-international-conference-on-informatics-in-control-automation-and-robotics

Conference

Conference14th International Conference on Informatics in Control, Automation and Robotics
Abbreviated titleICINCO 2017
CountrySpain
CityMadrid
Period26/07/17 → 28/07/17
Internet address

Fingerprint

Tires
Kalman filters
Axles
Sensors
State estimation
Wheels
Tuning
Innovation
Stiffness
Neural networks
Computer simulation

Keywords

  • Virtual Sensors
  • Neural Networks
  • Adaptive Kalman Filter
  • Unscented Kalman Filter
  • Tire force estimation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering

Cite this

Acosta, M., Kanarachos, S., & Fitzpatrick, M. E. (2017). A Virtual Sensor for Integral Tire Force Estimation using Tire Model-Less Approaches and Adaptive Unscented Kalman Filter. In ICINCO 2017 - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics (Vol. 1, pp. 386-397). SciTePress. https://doi.org/10.5220/0006394103860397

A Virtual Sensor for Integral Tire Force Estimation using Tire Model-Less Approaches and Adaptive Unscented Kalman Filter. / Acosta, Manuel; Kanarachos, Stratis; Fitzpatrick, Michael E.

ICINCO 2017 - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics. Vol. 1 SciTePress, 2017. p. 386-397.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding

Acosta, M, Kanarachos, S & Fitzpatrick, ME 2017, A Virtual Sensor for Integral Tire Force Estimation using Tire Model-Less Approaches and Adaptive Unscented Kalman Filter. in ICINCO 2017 - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics. vol. 1, SciTePress, pp. 386-397, 14th International Conference on Informatics in Control, Automation and Robotics, Madrid, Spain, 26/07/17. https://doi.org/10.5220/0006394103860397
Acosta M, Kanarachos S, Fitzpatrick ME. A Virtual Sensor for Integral Tire Force Estimation using Tire Model-Less Approaches and Adaptive Unscented Kalman Filter. In ICINCO 2017 - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics. Vol. 1. SciTePress. 2017. p. 386-397 https://doi.org/10.5220/0006394103860397
Acosta, Manuel ; Kanarachos, Stratis ; Fitzpatrick, Michael E. / A Virtual Sensor for Integral Tire Force Estimation using Tire Model-Less Approaches and Adaptive Unscented Kalman Filter. ICINCO 2017 - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics. Vol. 1 SciTePress, 2017. pp. 386-397
@inproceedings{cd0b8ace3ff04dbda74f47ab39cb8bd0,
title = "A Virtual Sensor for Integral Tire Force Estimation using Tire Model-Less Approaches and Adaptive Unscented Kalman Filter",
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 virtual sensor is tested in aggressive and steady-state maneuvers, exhibiting in both cases a remarkable performance.",
keywords = "Virtual Sensors, Neural Networks, Adaptive Kalman Filter, Unscented Kalman Filter, Tire force estimation",
author = "Manuel Acosta and Stratis Kanarachos and Fitzpatrick, {Michael E.}",
year = "2017",
month = "7",
day = "26",
doi = "10.5220/0006394103860397",
language = "English",
volume = "1",
pages = "386--397",
booktitle = "ICINCO 2017 - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics",
publisher = "SciTePress",

}

TY - GEN

T1 - A Virtual Sensor for Integral Tire Force Estimation using Tire Model-Less Approaches and Adaptive Unscented Kalman Filter

AU - Acosta, Manuel

AU - Kanarachos, Stratis

AU - Fitzpatrick, Michael E.

PY - 2017/7/26

Y1 - 2017/7/26

N2 - 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 virtual sensor is tested in aggressive and steady-state maneuvers, exhibiting in both cases a remarkable performance.

AB - 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 virtual sensor is tested in aggressive and steady-state maneuvers, exhibiting in both cases a remarkable performance.

KW - Virtual Sensors

KW - Neural Networks

KW - Adaptive Kalman Filter

KW - Unscented Kalman Filter

KW - Tire force estimation

UR - http://www.ieee-ras.org/component/rseventspro/event/1040-icinco-2017-international-conference-on-informatics-in-control-automation-and-robotics

UR - http://www.scitepress.org/DigitalLibrary/ProceedingsList.aspx

UR - http://www.scitepress.org/DigitalLibrary/ProceedingsDetails.aspx?ID=Hxr/q2f7PZ4=&t=1

U2 - 10.5220/0006394103860397

DO - 10.5220/0006394103860397

M3 - Conference proceeding

VL - 1

SP - 386

EP - 397

BT - ICINCO 2017 - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics

PB - SciTePress

ER -

Access to Document