Model-based observers for vehicle dynamics and tyre force prediction
Advanced control and driving assistance systems play a major role in modern vehicles, ensuring higher standards of safety and performance. Their correct operation extensively depends on the knowledge of tyre forces and vehicle drift. However, these quantities are hard to measure directly, due to cost or technological reasons. One possible alternative that is attracting much attention in the last few years is represented by virtual sensing where the quantities of interest can be inferred using a physical model that maps the relationship between these quantities and other available direct measurements, like accelerations, velocities and rate-of-turns. In this research, model-based observation is adopted to predict tyre forces and slip angles. In contrast to existing systems, ours relies on direct causality equations without the need of any explicit tyre model. Different observers are developed that are grounded, respectively, in the Cubature Kalman and Particle filtering, and they are contrasted against the standard Extended Kalman filter (EKF). Results are presented to quantitatively assess the performance of the observers using a 14 Degrees Of Freedom (DOFs) full vehicle model that has been simulated in standard manoeuvres including constant radius cornering, increasing and swept-sine steering, and sine-dwell manoeuvring. Although all three embodiments allow model nonlinearities and measurement noise to be appropriately tackled, the two Kalman filters outperform the PF in terms of estimation accuracy, especially for tyre force prediction. In addition, the novel Cubature Kalman filter shows comparable accuracy and robustness, but higher stability when compared to the EKF.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00423114
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Supplemental Notes:
- © 2021 Informa UK Limited, trading as Taylor & Francis Group 2021. Abstract reprinted with permission of Taylor & Francis.
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Authors:
- Reina, Giulio
- Leanza, Antonio
- Mantriota, Giacomo
- Publication Date: 2022-8
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 2845-2870
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Serial:
- Vehicle System Dynamics
- Volume: 60
- Issue Number: 8
- Publisher: Taylor & Francis
- ISSN: 0042-3114
- EISSN: 1744-5159
- Serial URL: https://www.tandfonline.com/toc/nvsd20/current
Subject/Index Terms
- TRT Terms: Advanced vehicle control systems; Kalman filtering; Motor vehicle dynamics; Predictive models; Tire forces; Wheel slip
- Subject Areas: Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01855941
- Record Type: Publication
- Files: TRIS
- Created Date: Aug 24 2022 3:05PM