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, Alexander and Knight, Justin M. and Sun, He and Fogarty, Kevin (2021) Data-driven subspace predictive control: lab demonstration and future outlook. In: Techniques and Instrumentation for Detection of Exoplanets X. Proceedings of SPIE. No.11823. Society of Photo-Optical Instrumentation Engineers , Bellingham, WA, Art. No. 1182306. ISBN 9781510644847. https://resolver.caltech.edu/CaltechAUTHORS:20220615-220721508
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Abstract
The search for exoplanets is pushing adaptive optics systems on ground-based telescopes to their limits. A major limitation is the temporal error of the adaptive optics systems. The temporal error can be reduced with predictive control. We use a linear data-driven integral predictive controller that learns while running in closed-loop. This is a new algorithm that has recently been developed. The controller is tested in the lab with MagAO-X under various conditions, where we gain several orders of magnitude in contrast compared to a classic integrator. We will present the lab results, and we will show how this controller can be implemented with current hardware for future extremely large telescopes.
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| Additional Information: | © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE). 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. This research made use of HCIPy, an open-source object-oriented framework written in Python for performing end-to-end simulations of high-contrast imaging instruments. | ||||||||||||||||||||||
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| Subject Keywords: | high-contrast imaging, high-resolution spectroscopy, exoplanets, adaptive optics | ||||||||||||||||||||||
| Series Name: | Proceedings of SPIE | ||||||||||||||||||||||
| Issue or Number: | 11823 | ||||||||||||||||||||||
| DOI: | 10.1117/12.2594875 | ||||||||||||||||||||||
| Record Number: | CaltechAUTHORS:20220615-220721508 | ||||||||||||||||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20220615-220721508 | ||||||||||||||||||||||
| Official Citation: | Sebastiaan Y. Haffert, Jared R. Males, Laird Close, Joseph Long, Lauren Schatz, Kyle van Gorkom, Alexander Hedglen, Jennifer Lumbres, Alexander Rodack, Olivier Guyon, Justin Knight, Maggie Kautz, and Logan Pearce "Data-driven subspace predictive control: lab demonstration and future outlook", Proc. SPIE 11823, Techniques and Instrumentation for Detection of Exoplanets X, 1182306 (1 September 2021); https://doi.org/10.1117/12.2594875 | ||||||||||||||||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||||||||||||
| ID Code: | 115174 | ||||||||||||||||||||||
| Collection: | CaltechAUTHORS | ||||||||||||||||||||||
| Deposited By: | Tony Diaz | ||||||||||||||||||||||
| Deposited On: | 17 Jun 2022 20:15 | ||||||||||||||||||||||
| Last Modified: | 17 Jun 2022 20:15 |
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