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
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;
Conference_Titel :
Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-9505-0
DOI :
10.1109/ROBOT.2006.1642077
