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Semi-parametric Regression based on Machine Learning Methods for UAS Stall Identification

Abstract : A semi-parametric regression methodology is formulated to identify the unsteady lift characteristics of a small UAS undergoing dynamic stall. Based on the trailing edge separation model of Leishmann and Beddoes, the nonlinear evolution of the separation point is formulated so that it can be estimated by non-parametric Machine Learning methods. Validation of the methodology is presented with the identification of the lift coefficient based on quasi-steady wind tunnel tests.
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https://hal-enac.archives-ouvertes.fr/hal-03286034
Contributor : Vincent Guibert Connect in order to contact the contributor
Submitted on : Tuesday, July 13, 2021 - 6:06:04 PM
Last modification on : Tuesday, October 19, 2021 - 11:02:55 AM
Long-term archiving on: : Thursday, October 14, 2021 - 7:20:41 PM

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Vincent Guibert, Mathieu Brunot, Murat Bronz, Jean-Philippe Condomines. Semi-parametric Regression based on Machine Learning Methods for UAS Stall Identification. 19th IFAC Symposium on System Identification, Jul 2021, Padova (virtual), Italy. ⟨hal-03286034⟩

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