Identification and control of a small-scale helicopter
Document Type
Article
Publication Date
1-1-2010
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.
Keywords
Dynamics model, System identification, Black box, Small-scale helicopter, Neural networks (NNs), Control design
Publication Title
Journal of Zhejiang University Science A
Volume
11
Issue
12
Publisher
Springer Verlag (Germany)
Publisher Location
EDITORIAL BOARD, 20 YUGU RD, HANGZHOU, 310027, PEOPLES R CHINA