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

Vincent Guibert; Mathieu Brunot; Murat Bronz; Jean-Philippe Condomines

Conference papers

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

Vincent Guibert ^{1, 2} Mathieu Brunot ¹ Murat Bronz ² Jean-Philippe Condomines ²

1 ONERA / DTIS, Université de Toulouse [Toulouse]

2 ENAC - Ecole Nationale de l'Aviation Civile

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.

Keywords : Nonlinear system identification Machine learning Grey box modelling Unmanned aircraft system Stall modelling Non-parametric methods

Document type :

Conference papers

Domain :

Engineering Sciences [physics] / Automatic

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https://hal-enac.archives-ouvertes.fr/hal-03286034
Contributor : Vincent Guibert <>
Submitted on : Tuesday, July 13, 2021 - 6:06:04 PM
Last modification on : Wednesday, July 21, 2021 - 3:50:39 PM

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

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

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