Published September 1, 2021
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Data-driven subspace predictive control: lab demonstration and future outlook
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.
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.Attached Files
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Additional details
Identifiers
- Eprint ID
- 115174
- Resolver ID
- CaltechAUTHORS:20220615-220721508
Funding
- NASA Hubble Fellowship
- HST-HF2-51436.001-A
- NASA
- NAS5-26555
Dates
- Created
-
2022-06-17Created from EPrint's datestamp field
- Updated
-
2022-06-17Created from EPrint's last_modified field
Caltech Custom Metadata
- Series Name
- Proceedings of SPIE
- Series Volume or Issue Number
- 11823