DocumentCode
2100808
Title
Unmanned aerial vehicle (UAV) modelling based on supervised neural networks
Author
San Martin, R. ; Barrientos, A. ; Gutierrez, P. ; Del Cerro, J.
Author_Institution
Dpto. Ing. Sistemas y Autom., Univ. Politecnica de Madrid
fYear
2006
fDate
15-19 May 2006
Firstpage
2497
Lastpage
2502
Abstract
This paper proposes the utilization of hybrid models of supervised neural networks for the modelling of dynamic systems. Particularly, as an example of a system, an autonomous helicopter or UAV is identified in both attitude and position systems. The evaluation of the model is done by comparing the radial basis and multilayer perceptron with the real system
Keywords
aerospace robotics; aircraft control; attitude control; helicopters; mobile robots; multilayer perceptrons; position control; radial basis function networks; remotely operated vehicles; telerobotics; attitude system; autonomous helicopter; dynamic systems; multilayer perceptron; position system; radial basis network; supervised neural networks; unmanned aerial vehicle modelling; Aerodynamics; Embedded system; Helicopters; Mathematical model; Multilayer perceptrons; Neural networks; Power system modeling; Remotely operated vehicles; Unmanned aerial vehicles; Vehicle dynamics;
fLanguage
English
Publisher
ieee
Conference_Titel
Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on
Conference_Location
Orlando, FL
ISSN
1050-4729
Print_ISBN
0-7803-9505-0
Type
conf
DOI
10.1109/ROBOT.2006.1642077
Filename
1642077
Link To Document