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Small-scale helicopter system identification model using recurrent neural networks

Designing a reliable flight control for an autonomous helicopter requires a high performance dynamics model. This paper studies the recurrent neural network nonlinear model identification of a small scale helicopter. We have selected a Nonlinear AutoRegressive with eXogenous Inputs Series- Paral...

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Main Authors: Taha, Z., Deboucha, A., Dahari, M.
Format: Conference or Workshop Item
Language:English
Published: 2010
Subjects:
T Technology (General)
Online Access:http://eprints.um.edu.my/11328/1/T5-3-2.pdf
http://eprints.um.edu.my/11328/
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spelling my.um.eprints.113282014-12-18T02:41:40Z http://eprints.um.edu.my/11328/ Small-scale helicopter system identification model using recurrent neural networks Taha, Z. Deboucha, A. Dahari, M. T Technology (General) Designing a reliable flight control for an autonomous helicopter requires a high performance dynamics model. This paper studies the recurrent neural network nonlinear model identification of a small scale helicopter. We have selected a Nonlinear AutoRegressive with eXogenous Inputs Series- Parallel (NARXSP) network model which identifies the dynamics model of an unmanned aerial helicopter from real flight data. The identification process is conducted by using the well known Levenberg-Marquardt learning algorithm. The obtained dynamics model shows good fitness with the actual data. This accuracy might be used to realize a reliable flight control for an autonomous helicopter. 2010-11 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.um.edu.my/11328/1/T5-3-2.pdf Taha, Z. and Deboucha, A. and Dahari, M. (2010) Small-scale helicopter system identification model using recurrent neural networks. In: Trends in Electronics Conference, 21-24 Nov 2010, Fukuoka, Japan.
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Taha, Z.
Deboucha, A.
Dahari, M.
Small-scale helicopter system identification model using recurrent neural networks
description Designing a reliable flight control for an autonomous helicopter requires a high performance dynamics model. This paper studies the recurrent neural network nonlinear model identification of a small scale helicopter. We have selected a Nonlinear AutoRegressive with eXogenous Inputs Series- Parallel (NARXSP) network model which identifies the dynamics model of an unmanned aerial helicopter from real flight data. The identification process is conducted by using the well known Levenberg-Marquardt learning algorithm. The obtained dynamics model shows good fitness with the actual data. This accuracy might be used to realize a reliable flight control for an autonomous helicopter.
format Conference or Workshop Item
author Taha, Z.
Deboucha, A.
Dahari, M.
author_facet Taha, Z.
Deboucha, A.
Dahari, M.
author_sort Taha, Z.
title Small-scale helicopter system identification model using recurrent neural networks
title_short Small-scale helicopter system identification model using recurrent neural networks
title_full Small-scale helicopter system identification model using recurrent neural networks
title_fullStr Small-scale helicopter system identification model using recurrent neural networks
title_full_unstemmed Small-scale helicopter system identification model using recurrent neural networks
title_sort small-scale helicopter system identification model using recurrent neural networks
publishDate 2010
url http://eprints.um.edu.my/11328/1/T5-3-2.pdf
http://eprints.um.edu.my/11328/
_version_ 1643689028600463360
score 13.211869

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