Abstract
ABSTRACTThis paper considers identification of all significant vehicle handling dynamics of a test vehicle, including identification of a combined-slip tyre model, using only those sensors currently available on most vehicle controller area network buses. Using an appropriately simple but efficient model structure, all of the independent parameters are found from test vehicle data, with the resulting model accuracy demonstrated on independent validation data. The paper extends previous work on augmented Kalman Filter state estimators to concentrate wholly on parameter identification. It also serves as a review of three alternative filtering methods; identifying forms of the unscented Kalman filter, extended Kalman filter and particle filter are proposed and compared for effectiveness, complexity and computational efficiency. All three filters are suited to applications of system identification and the Kalman Filters can also operate in real-time in on-line model predictive controllers or estimators.
Highlights
Advanced automotive control requires accurate dynamic models to predict vehicle response
This paper considers identification of all significant vehicle handling dynamics of a test vehicle, including identification of a combinedslip tyre model, using only those sensors currently available on most vehicle controller area network buses
Using an appropriately simple but efficient model structure, all of the independent parameters are found from test vehicle data, with the resulting model accuracy demonstrated on independent validation data
Summary
Advanced automotive control requires accurate dynamic models to predict vehicle response. The particle filter (PF) belongs to the group of recursive Monte Carlo methods and is suited to harsh nonlinearities and non-Gaussian applications [15] It approximates the posterior probability density function (pdf) of the state vector in a similar way to the UKF, but uses a much larger set of samples, which are randomly selected from an initial uniform distribution. A simplified case is presented – identification of the front and rear tyre stiffness of a linear single-track model, based on test vehicle data This example serves as an immediate and straightforward comparison of the different techniques, illustrating how they are tuned for performance and efficiency. The major study of identification and validation of the more complex four degree of freedom full vehicle handling model completes the paper
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