Share Email Print

Proceedings Paper

Neural-network-based control of large structures
Author(s): Srinivas R. Vadali; Tarunraj Singh; Andrew J. Kurdila; John L. Junkins
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Adaptive control of spacecraft and large structures using Radial Basis Function (RBF) based Artificial Neural Networks (ANN) is investigated. The centers of approximation of the RBFs are allowed to be dynamic in order to provide persistent excitation and a small window of approximation. Both state and time based RBFs are investigated for their ability to identify unmodeled and persistent effects. Integral feedback of the attitude seems necessary, especially when the initial estimates are poor, to eliminate steady state errors for pointing applications. Examples of motion-to-rest as well as tracking maneuvers and vibration suppression are presented.

Paper Details

Date Published: 8 September 1993
PDF: 12 pages
Proc. SPIE 1917, Smart Structures and Materials 1993: Smart Structures and Intelligent Systems, (8 September 1993); doi: 10.1117/12.152819
Show Author Affiliations
Srinivas R. Vadali, Texas A&M Univ. (United States)
Tarunraj Singh, Texas A&M Univ. (United States)
Andrew J. Kurdila, Texas A&M Univ. (United States)
John L. Junkins, Texas A&M Univ. (United States)

Published in SPIE Proceedings Vol. 1917:
Smart Structures and Materials 1993: Smart Structures and Intelligent Systems
Nesbitt W. Hagood; Gareth J. Knowles, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?