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Decision tree classifiers for unmanned aircraft configuration selection

João Antônio Dantas de Jesus Ferreira (Department of Aeronautical Engineering, Instituto Tecnológico de Aeronáutica, São José dos Campos, Brazil)
Ney Rafael Secco (Department of Aeronautical Engineering, Instituto Tecnológico de Aeronáutica, São José dos Campos, Brazil)

Aircraft Engineering and Aerospace Technology

ISSN: 0002-2667

Article publication date: 26 July 2021

Issue publication date: 12 August 2021

107

Abstract

Purpose

This paper aims to investigate the possibility of lowering the time taken during the aircraft design for unmanned aerial vehicles by using machine learning (ML) for the configuration selection phase. In this work, a database of unmanned aircraft is compiled and is proposed that decision tree classifiers (DTC) can understand the relations between mission and operational requirements and the resulting aircraft configuration.

Design/methodology/approach

This paper presents a ML-based approach to configuration selection of unmanned aircraft. Multiple DTC are built to predict the overall configuration. The classifiers are trained with a database of 118 unmanned aircraft with 57 characteristics, 47 of which are inputs for the classification problem, and 10 are the desired outputs, such as wing configuration or engine type.

Findings

This paper shows that DTC can be used for the configuration selection of unmanned aircraft with reasonable accuracy, understanding the connections between the different mission requirements and the culminating configuration. The framework is also capable of dealing with incomplete databases, maximizing the available knowledge.

Originality/value

This paper increases the computational usage for the aircraft design while retaining requirements’ traceability and increasing decision awareness.

Keywords

Citation

Dantas de Jesus Ferreira, J.A. and Secco, N.R. (2021), "Decision tree classifiers for unmanned aircraft configuration selection", Aircraft Engineering and Aerospace Technology, Vol. 93 No. 6, pp. 1122-1132. https://doi.org/10.1108/AEAT-03-2021-0074

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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