Share Email Print
cover

Proceedings Paper

Adaptive gain in closed-loop tilt control and adaptive optics
Author(s): Dennis A. Montera; James M. Brown; Odell R. Reynolds; Miles D. Buckman; Darryl J. Sanchez; Denis W. Oesch; Erica M. Hoeffner; Michael W. Bishop; Brian T. Kay; Tyler J. Hardy
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The performance of closed-loop tilt-control and adaptive-optics systems suffers when conditions change. Examples of changing conditions are angular extent of the object, signal-to-noise ratio, and characteristics of the disturbance. A simple learning algorithm motivated by neural network theory is developed to change the closed-loop gain in real-time to adapt quickly to changing conditions. This technique finds the correct loop gain within seconds with no operator intervention, which saves several minutes for each observation. Simulation and experimental results show improvement for both tilt-control and adaptive-optics systems.

Paper Details

Date Published: 10 July 2018
PDF: 14 pages
Proc. SPIE 10703, Adaptive Optics Systems VI, 107031H (10 July 2018); doi: 10.1117/12.2310193
Show Author Affiliations
Dennis A. Montera, Air Force Research Lab. (United States)
James M. Brown, Leidos, Inc. (United States)
Odell R. Reynolds, Air Force Research Lab. (United States)
Miles D. Buckman, Air Force Research Lab. (United States)
Darryl J. Sanchez, Air Force Research Lab. (United States)
Denis W. Oesch, Leidos, Inc. (United States)
Erica M. Hoeffner, Leidos, Inc. (United States)
Michael W. Bishop, Air Force Research Lab. (United States)
Brian T. Kay, Air Force Research Lab. (United States)
Tyler J. Hardy, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 10703:
Adaptive Optics Systems VI
Laird M. Close; Laura Schreiber; Dirk Schmidt, Editor(s)

© SPIE. Terms of Use
Back to Top