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Neural-empirical tyre model based on recursive lazy learning under combined longitudinal and lateral slip conditions.

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Title: Neural-empirical tyre model based on recursive lazy learning under combined longitudinal and lateral slip conditions.
Authors: Boada, M.1 mjboada@ing.uc3m.es, Boada, B.1, Garcia-Pozuelo, D.1, Diaz, V.1
Source: International Journal of Automotive Technology. Dec2011, Vol. 12 Issue 6, p821-829. 9p.
Subjects: Automobile tires, Artificial neural networks, Mechanics (Physics), Complexity (Philosophy), Artificial intelligence
Abstract: The behaviour of the tyre plays an important role in the vehicle handling. An accurate tyre model that estimates these forces and moments it is highly essential for the studies of vehicle behaviour. For the last ten years neural networks have attracted a great deal of attention in vehicle dynamics and control. Neural networks have been effectively applied to model complex systems due to their good learning capability. In this paper a recursive lazy learning method based on neural networks is considered to model the tyre characteristics under combined braking and cornering. The proposed method is validated by comparison with experimental obtained responses. Results show the estimated model correlates very well with the data obtained experimentally. Moreover, the neural model proposed allows to include the asymetric tyre behaviour in the tyre model without difficulty. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Automotive Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Automotive+Technology%22">International Journal of Automotive Technology</searchLink>. Dec2011, Vol. 12 Issue 6, p821-829. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Automobile+tires%22">Automobile tires</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanics+%28Physics%29%22">Mechanics (Physics)</searchLink><br /><searchLink fieldCode="DE" term="%22Complexity+%28Philosophy%29%22">Complexity (Philosophy)</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink>
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  Label: Abstract
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  Data: The behaviour of the tyre plays an important role in the vehicle handling. An accurate tyre model that estimates these forces and moments it is highly essential for the studies of vehicle behaviour. For the last ten years neural networks have attracted a great deal of attention in vehicle dynamics and control. Neural networks have been effectively applied to model complex systems due to their good learning capability. In this paper a recursive lazy learning method based on neural networks is considered to model the tyre characteristics under combined braking and cornering. The proposed method is validated by comparison with experimental obtained responses. Results show the estimated model correlates very well with the data obtained experimentally. Moreover, the neural model proposed allows to include the asymetric tyre behaviour in the tyre model without difficulty. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Automotive Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Mechanics (Physics)
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              Text: Dec2011
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