Table of Contents
ISRN Mechanical Engineering
Volume 2011, Article ID 213582, 8 pages
http://dx.doi.org/10.5402/2011/213582
Research Article

Neural Networks-Based Identification and Control of a Large Flexible Antenna

1Department of Human and Information Systems Engineering, Gifu University, Gifu 501-1193, Japan
2Department of Mechanical Systems Engineering, Gifu University, Gifu 501-1193, Japan
3National Astronomical Observatory of Japan, Tokyo 181-0015, Japan

Received 21 June 2011; Accepted 8 August 2011

Academic Editor: S. Marchesiello

Copyright © 2011 Minoru Sasaki et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents identification and control of a 10-m antenna via accelerometers and angle encoder data. Artificial neural networks can be used effectively for the identification and control of nonlinear dynamical system such as a large flexible antenna with a friction drive system. Some identification results are shown and compared with the results of conventional prediction error method. And we use a neural network inverse model to control the large flexible antenna. In the neural network inverse model, a neural network is trained, using supervised learning, to develop an inverse model of the antenna. The network input is the process output, and the network output is the corresponding process input. The control results show the validation of the ANN approach for identification and control of the 10-m flexible antenna.