James, S. and Anderson, S.R. orcid.org/0000-0002-7452-5681 (2018) Linear system identification of longitudinal vehicle dynamics versus nonlinear physical modelling. In: Proceedings of 2018 UKACC 12th International Conference on Control (CONTROL). Control 2018: The 12th International UKACC Conference on Control, 05-07 Sep 2018, Sheffield, UK. IEEE , pp. 146-151. ISBN 978-1-5386-2864-5
Abstract
Mathematical modelling of vehicle dynamics is essential for the development of autonomous cars. Many of the vehicle models that are used for control design in cars are based on nonlinear physical models. However, it is not clear, especially for the case of longitudinal dynamics, whether such nonlinear models are necessary or simpler models can be used. In this paper, we identify a linear data-driven model of longitudinal vehicle dynamics and compare it to a nonlinear physically derived model. The linear model was identified in continuous-time state-space form using a prediction error method. The identification data were obtained from a Lancia Delta car, over 53 km of normal driving on public roads. The selected linear model was first order with requested torque, brake and road gradient as inputs and car velocity as output. The key results were that 1. the linear model was accurate, with a variance accounted for (VAF) metric of VAF=96.5%, and 2. the identified linear model was also superior in accuracy to the nonlinear physical model, VAF=77.4%. The implication of these results, therefore, is that for longitudinal dynamics, in normal driving conditions, a first order linear model is sufficient to describe the vehicle dynamics. This is advantageous for control design, state estimation and real-time implementation, e.g. in predictive control.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Torque; Vehicle dynamics; Roads; Engines; Predictive models; Data models |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Jul 2018 16:00 |
Last Modified: | 05 Dec 2018 14:36 |
Published Version: | https://doi.org/10.1109/CONTROL.2018.8516756 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/CONTROL.2018.8516756 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:133010 |