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Proceedings Paper

Neural network control of the high-contrast imaging system
Author(s): He Sun; N. Jeremy Kasdin
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Paper Abstract

Currently, linear state space modeling is used for focal plane wavefront estimation and control of high-contrast imaging system. Although this framework has made great strides in the past decades, it fails to track the nonlinearities from the deformable mirrors and the light propagation, which to some extent influences the accuracy of the electric field estimation and the speed and robustness of the controller. In this paper, we propose the application of neural networks to identify and optimally control a high-contrast imaging system. Based on the E-M algorithm and reinforcement learning techniques, we develop a new nonlinear system identificaton method and a corresponding nonlinear neural network controller. Simulation and experimental results from Princetons High Contrast Imaging Lab (HCIL) are reported to demonstrate the utility of this algorithm.

Paper Details

Date Published: 6 July 2018
PDF: 10 pages
Proc. SPIE 10698, Space Telescopes and Instrumentation 2018: Optical, Infrared, and Millimeter Wave, 106981R (6 July 2018); doi: 10.1117/12.2312356
Show Author Affiliations
He Sun, Princeton Univ. (United States)
N. Jeremy Kasdin, Princeton Univ. (United States)

Published in SPIE Proceedings Vol. 10698:
Space Telescopes and Instrumentation 2018: Optical, Infrared, and Millimeter Wave
Makenzie Lystrup; Howard A. MacEwen; Giovanni G. Fazio; Natalie Batalha; Nicholas Siegler; Edward C. Tong, Editor(s)

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