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Vehicle tyre and handling model identification using an extended Kalman filter
| Content Provider | Semantic Scholar |
|---|---|
| Author | Best, M. C. Newton, Andrew |
| Copyright Year | 2008 |
| Abstract | This paper uses an Extended Kalman filter in an unusual way to identify a vehicle handling model and its associated tyre model. The method can be applied as an off-line batch process, or in real-time; here we concentrate on batch analysis of data from a Jaguar XJ test vehicle. The Identifying Extended Kalman Filter (IEKF) uses the full state measurement that is available from combination GPS / inertia instrumentation packs. Previous IEKF studies have shown success in identifying a bicycle model with a tyre force function for each axle. This paper extends to identification of a single, load dependent tyre model which applies to all four wheelstations, identified within a yaw-roll-sideslip model structure. The resulting model provides impressive open-loop state replication, including accurate tyre slip prediction across the fully nonlinear slip range of the tyre. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://dspace.lboro.ac.uk/dspace-jspui/bitstream/2134/8320/1/MCB_4_18.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
