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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: 3673

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2010 Vol.11 No.12 P.978-985

http://doi.org/10.1631/jzus.A1001368


Identification and control of a small-scale helicopter


Author(s):  Abdelhakim Deboucha, Zahari Taha

Affiliation(s):  Centre for Product Design and Manufacturing, University of Malaya, 50603 Kuala Lumpur, Malaysia, Faculty of Manufacturing Engineering and Management Technology, University Malaysia Pahang, 26300 Gambang, Pahang, Malaysia

Corresponding email(s):   eng.hakim25@gmail.com, zaharitaha@ump.edu.my

Key Words:  Dynamics model, System identification, Black box, Small-scale helicopter, Neural networks (NNs), Control design


Abdelhakim Deboucha, Zahari Taha. Identification and control of a small-scale helicopter[J]. Journal of Zhejiang University Science A, 2010, 11(12): 978-985.

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T1 - Identification and control of a small-scale helicopter
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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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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