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Proceedings Paper

Model inversion tracking control for UltraLITE using neural networks
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Paper Abstract

This paper presents the analytical methodology and initial numerical simulation results for autonomous neural control of the Ultra-Lightweight Imaging Technology Experiment (UltraLITE) Phase I test article. The UltraLITE Phase I test article is a precision deployable structure currently under development at the United States Air Force Research Laboratory (AFRL). Its purpose is to examine control and hardware integration issues related to large deployable sparse optical array spacecraft systems. In this paper, a multi-stage control architecture is examined which incorporates artificial neural networks for model inversion tracking control. The emphasis in the control design approach is to exploit the known nonlinear dynamics of the system in the synthesis of a model inversion tracking controller and to augment the nonlinear controller with an adaptive neuro-controller to accommodate for changing dynamics, failures, and model uncertainties.

Paper Details

Date Published: 8 December 1998
PDF: 9 pages
Proc. SPIE 3430, Novel Optical Systems and Large-Aperture Imaging, (8 December 1998); doi: 10.1117/12.332478
Show Author Affiliations
Jesse Leitner, Air Force Research Lab. (United States)
Keith K. Denoyer, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 3430:
Novel Optical Systems and Large-Aperture Imaging
Kevin Dean Bell; Michael K. Powers; Jose M. Sasian, Editor(s)

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