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Vehicle Dynamics Virtual Sensing Using Unscented Kalman Filter: Simulations and Experiments in a Driver-in-the-Loop Setup

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Informatics in Control, Automation and Robotics (ICINCO 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 495))

Abstract

Chassis Active Safety Systems require access to a set of vehicle dynamics motion states which measurement is neither trivial nor cost-effective (e.g. lateral velocity). In this work, virtual sensing is applied to vehicle dynamics and proposed as a cost-effective solution to infer the vehicle planar motion states and three-axis tyre forces from signals measured by inexpensive sensors. Specifically, the tyre longitudinal forces are estimated using Adaptive Random-Walk Linear Kalman Filters and the vehicle planar motion states are determined in a hybrid state estimator formed by an Unscented Kalman Filter and Feedforward Neural Networks. The tyre vertical forces are estimated using a quasi-static weight transfer approach and Recursive Least Squares. The complete structure is integrated into a modular fashion and tested experimentally using a driver-in-the-loop setup. An extensive catalogue of manoeuvres is executed by a real driver to evidence the performance of the proposed virtual sensor at the limits of handling.

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Acknowledgement

This research is part of the Interdisciplinary Training Network in Multi-actuated Ground Vehicles (ITEAM) and has received funding from the European Unions Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No: 675999. M. E. Fitzpatrick is grateful for funding from the Lloyds Register Foundation, a charitable foundation helping to protect life and property by supporting engineering-related education, public engagement and the application of research.

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Authors and Affiliations

  1. Coventry University, Priory Street, Coventry, United Kingdom

    Manuel Acosta, Stratis Kanarachos & Michael E. Fitzpatrick

Authors
  1. Manuel Acosta
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  2. Stratis Kanarachos
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  3. Michael E. Fitzpatrick
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Corresponding author

Correspondence to Manuel Acosta .

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Editors and Affiliations

  1. Ford Research and Advanced Engineering, Dearborn, MI, USA

    Oleg Gusikhin

  2. University of Paris-EST Créteil (UPEC), Vitry sur Seine, France

    Kurosh Madani

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Acosta, M., Kanarachos, S., Fitzpatrick, M.E. (2020). Vehicle Dynamics Virtual Sensing Using Unscented Kalman Filter: Simulations and Experiments in a Driver-in-the-Loop Setup. In: Gusikhin, O., Madani, K. (eds) Informatics in Control, Automation and Robotics . ICINCO 2017. Lecture Notes in Electrical Engineering, vol 495. Springer, Cham. https://doi.org/10.1007/978-3-030-11292-9_29

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