Direct adaptive control of flexible space structures using neural networks

Author

Maund, M.P. ; Helferty, J.J. ; Boussalis, D. ; Wang, S.J.

Author_Institution

Temple Univ., Philadelphia, PA, USA

Volume

2

fYear

1992

fDate

7-11 Jun 1992

Firstpage

844

Abstract

A method for using artificial neural networks for the direct adaptive control of large space structures (LSS) with partially unknown dynamics is investigated. A neuromorphic controller is developed, and the authors demonstrate how it is applied to the vibration suppression problem for LSS. A key result is that measurements of all of the system states are not required, but rather only the output measurements and their delayed values. The neuromorphic controller (NMC) is represented by a fixed topology feedforward neural network whose weights are adjusted in real-time by a nonlinear recursive least square algorithm. Several simulation examples are given for the problem of vibration suppression for a subsystem model of the Jet Propulsion Laboratory/AFAL flexible spacecraft simulator

Keywords

adaptive control; aerospace control; feedforward neural nets; large-scale systems; least squares approximations; vibration control; direct adaptive control; fixed topology feedforward neural network; flexible spacecraft simulator; large space structures; neural networks; neuromorphic controller; nonlinear recursive least square algorithm; vibration suppression; Adaptive control; Artificial neural networks; Delay; Feedforward neural networks; Least squares methods; Network topology; Neural networks; Neuromorphics; Propulsion; Vibration control;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1992. IJCNN., International Joint Conference on

Conference_Location

Baltimore, MD

Print_ISBN

0-7803-0559-0

Type

conf

DOI

10.1109/IJCNN.1992.226882

Filename

226882