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

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

13 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 201728 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
Period26/07/1728/07/17
Internet address

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

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    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