Several architectures exist to measure aerodynamic angles based on physical sensors. As far as unmanned aerial vehicle (UAV) is concerned, traditional systems hardly comply with reliability and redundancy requirements due to size and weight limitations. A patented virtual sensor, based on artificial neural network (ANN) techniques, named smart-air data, attitude and heading reference system (Smart-ADAHRS) has been investigated as a good estimator for aerodynamic angles in simulated environment. This paper focuses on flight testing procedures in operative environment and data processing for the Smart-ADAHRS validation with real data. As many factors interfere during the generation of the ANN training set, an accurate choice and integration of the flight test instrumentation (FTI) system components becomes crucial. A comprehensive description has been included about the FTI equipment and its influence on the neural network performance. Differences between numerical simulation and operative environment data are detailed as final aim of this work. At the end, feasible solutions are suggested to solve the typical gap between virtual and real scenario, both in terms of data analysis and neural network architecture.

Aerodynamic angle estimation: comparison between numerical results and operative environment data / Lerro, Angelo; Battipede, Manuela; Gili, Piero; Brandl, Alberto. - In: CEAS AERONAUTICAL JOURNAL. - ISSN 1869-5582. - ELETTRONICO. - (2019), pp. 1-14. [10.1007/s13272-019-00417-x]

Aerodynamic angle estimation: comparison between numerical results and operative environment data

Lerro, Angelo;Battipede, Manuela;Gili, Piero;Brandl, Alberto
2019

Abstract

Several architectures exist to measure aerodynamic angles based on physical sensors. As far as unmanned aerial vehicle (UAV) is concerned, traditional systems hardly comply with reliability and redundancy requirements due to size and weight limitations. A patented virtual sensor, based on artificial neural network (ANN) techniques, named smart-air data, attitude and heading reference system (Smart-ADAHRS) has been investigated as a good estimator for aerodynamic angles in simulated environment. This paper focuses on flight testing procedures in operative environment and data processing for the Smart-ADAHRS validation with real data. As many factors interfere during the generation of the ANN training set, an accurate choice and integration of the flight test instrumentation (FTI) system components becomes crucial. A comprehensive description has been included about the FTI equipment and its influence on the neural network performance. Differences between numerical simulation and operative environment data are detailed as final aim of this work. At the end, feasible solutions are suggested to solve the typical gap between virtual and real scenario, both in terms of data analysis and neural network architecture.
File in questo prodotto:
File Dimensione Formato  
main.pdf

Open Access dal 05/09/2020

Descrizione: Post-print dell'articolo principale
Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: Pubblico - Tutti i diritti riservati
Dimensione 4.17 MB
Formato Adobe PDF
4.17 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2757652
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo