Identification and control of a small-scale helicopter
Designing reliable flight control for an autonomous helicopter requires a high performance dynamics model. In this paper, a nonlinear autoregressive with exogenous inputs (NLARX) model is selected as the mathematical structure for identifying and controlling the flight of a small-scale helicopter. A...
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Springer Verlag (Germany)
2010
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my.um.eprints.118912017-07-08T07:15:30Z http://eprints.um.edu.my/11891/ Identification and control of a small-scale helicopter Deboucha, A. Taha, Z. Q Science (General) Designing reliable flight control for an autonomous helicopter requires a high performance dynamics model. In this paper, a nonlinear autoregressive with exogenous inputs (NLARX) model is selected as the mathematical structure for identifying and controlling the flight of a small-scale helicopter. A neural network learning algorithm is combined with the NLARX model to identify the dynamic component of the rotorcraft unmanned aerial vehicle (RUAV). This identification process is based on the well-known gradient descent learning algorithm. As a case study, the multiple-input multiple-output (MIMO) model predictive control (MPC) is applied to control the pitch motion of the helicopter. Results of the neural network output model are closely match with the real flight data. The MPC also shows good performance under various conditions. Springer Verlag (Germany) 2010 Article PeerReviewed Deboucha, A. and Taha, Z. (2010) Identification and control of a small-scale helicopter. Journal of Zhejiang University Science A, 11 (12). pp. 978-985. ISSN 1673-565X |
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Q Science (General) Deboucha, A. Taha, Z. Identification and control of a small-scale helicopter |
description |
Designing reliable flight control for an autonomous helicopter requires a high performance dynamics model. In this paper, a nonlinear autoregressive with exogenous inputs (NLARX) model is selected as the mathematical structure for identifying and controlling the flight of a small-scale helicopter. A neural network learning algorithm is combined with the NLARX model to identify the dynamic component of the rotorcraft unmanned aerial vehicle (RUAV). This identification process is based on the well-known gradient descent learning algorithm. As a case study, the multiple-input multiple-output (MIMO) model predictive control (MPC) is applied to control the pitch motion of the helicopter. Results of the neural network output model are closely match with the real flight data. The MPC also shows good performance under various conditions. |
format |
Article |
author |
Deboucha, A. Taha, Z. |
author_facet |
Deboucha, A. Taha, Z. |
author_sort |
Deboucha, A. |
title |
Identification and control of a small-scale helicopter |
title_short |
Identification and control of a small-scale helicopter |
title_full |
Identification and control of a small-scale helicopter |
title_fullStr |
Identification and control of a small-scale helicopter |
title_full_unstemmed |
Identification and control of a small-scale helicopter |
title_sort |
identification and control of a small-scale helicopter |
publisher |
Springer Verlag (Germany) |
publishDate |
2010 |
url |
http://eprints.um.edu.my/11891/ |
_version_ |
1643689171424903168 |