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Data-driven subspace predictive control of adaptive optics for high-contrast imaging

Haffert, Sebastiaan Y. and Males, Jared R. and Close, Laird M. and Van Gorkom, Kyle and Long, Joseph D. and Hedglen, Alexander D. and Guyon, Olivier and Schatz, Lauren and Kautz, Maggie and Lumbres, Jennifer and Rodack, Alex and Knight, Justin M. and Sun, He and Fogarty, Kevin (2021) Data-driven subspace predictive control of adaptive optics for high-contrast imaging. Journal of Astronomical Telescopes, Instruments, and Systems, 7 (2). Art. No. 029001. ISSN 2329-4124. doi:10.1117/1.jatis.7.2.029001.

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The search for exoplanets is pushing adaptive optics (AO) systems on ground-based telescopes to their limits. One of the major limitations at small angular separations, exactly where exoplanets are predicted to be, is the servo-lag of the AO systems. The servo-lag error can be reduced with predictive control where the control is based on the future state of the atmospheric disturbance. We propose to use a linear data-driven integral predictive controller based on subspace methods that are updated in real time. The new controller only uses the measured wavefront errors and the changes in the deformable mirror commands, which allows for closed-loop operation without requiring pseudo-open loop reconstruction. This enables operation with non-linear wavefront sensors such as the pyramid wavefront sensor. We show that the proposed controller performs near-optimal control in simulations for both stationary and non-stationary disturbances and that we are able to gain several orders of magnitude in raw contrast. The algorithm has been demonstrated in the lab with MagAO-X, where we gain more than two orders of magnitude in contrast.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Haffert, Sebastiaan Y.0000-0001-5130-9153
Males, Jared R.0000-0002-2346-3441
Close, Laird M.0000-0002-2167-8246
Long, Joseph D.0000-0003-1905-9443
Guyon, Olivier0000-0002-1097-9908
Schatz, Lauren0000-0002-5192-521X
Kautz, Maggie0000-0003-3253-2952
Lumbres, Jennifer0000-0002-3525-2262
Sun, He0000-0003-1526-6787
Fogarty, Kevin0000-0002-2691-2476
Additional Information:© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE). Paper 20143 received Sep. 18, 2020; accepted for publication Mar. 9, 2021; published online Apr. 6, 2021. Support for this work was provided by NASA through the NASA Hubble Fellowship grant #HST-HF2-51436.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. MagAO-X is funded by the NSF MRI program, award #1625441. The authors declare that they have no conflict of interest.
Funding AgencyGrant Number
NASA Hubble FellowshipHST-HF2-51436.001-A
Subject Keywords:adaptive optics; exoplanets; high-contrast imaging; coronagraph; spectroscopy
Issue or Number:2
Record Number:CaltechAUTHORS:20210821-164958966
Persistent URL:
Official Citation:Sebastiaan Y. Haffert, Jared R. Males, Laird M. Close, Kyle Van Gorkom, Joseph D. Long, Alexander D. Hedglen, Olivier Guyon, Lauren Schatz, Maggie Y. Kautz, Jennifer Lumbres, Alexander T. Rodack, Justin M. Knight, He Sun, and Kevin Fogarty "Data-driven subspace predictive control of adaptive optics for high-contrast imaging," Journal of Astronomical Telescopes, Instruments, and Systems 7(2), 029001 (6 April 2021).
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:110369
Deposited By: Tony Diaz
Deposited On:21 Aug 2021 17:24
Last Modified:21 Aug 2021 17:24

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