CLC number: V249.1; TP242
On-line Access: 2010-12-09
Received: 2010-10-28
Revision Accepted: 2010-10-29
Crosschecked: 2010-10-29
Cited: 3
Clicked: 6354
Abdelhakim Deboucha, Zahari Taha. Identification and control of a small-scale helicopter[J]. Journal of Zhejiang University Science A, 2010, 11(12): 978-985.
@article{title="Identification and control of a small-scale helicopter",
author="Abdelhakim Deboucha, Zahari Taha",
journal="Journal of Zhejiang University Science A",
volume="11",
number="12",
pages="978-985",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1001368"
}
%0 Journal Article
%T Identification and control of a small-scale helicopter
%A Abdelhakim Deboucha
%A Zahari Taha
%J Journal of Zhejiang University SCIENCE A
%V 11
%N 12
%P 978-985
%@ 1673-565X
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1001368
TY - JOUR
T1 - Identification and control of a small-scale helicopter
A1 - Abdelhakim Deboucha
A1 - Zahari Taha
J0 - Journal of Zhejiang University Science A
VL - 11
IS - 12
SP - 978
EP - 985
%@ 1673-565X
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1001368
Abstract: 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.
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