Presentation + Paper
1 September 2021 Data-driven subspace predictive control: lab demonstration and future outlook
Author Affiliations +
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. With the current schedule, the new data-driven predictive controller will be tested on-sky in spring 2021. We will present both the lab results and the on-sky results, and we will show how this controller can be implemented with current hardware for future extremely large telescopes.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sebastiaan Y. Haffert, Jared R. Males, Laird M. Close, Kyle van Gorkom, Joseph D. Long, Alexander D. Hedglen, Lauren Schatz, Jennifer Lumbres, Alexander Rodack, Justin M. Knight, He Sun, Kevin Fogarty, and Logan Pearce "Data-driven subspace predictive control: lab demonstration and future outlook", Proc. SPIE 11823, Techniques and Instrumentation for Detection of Exoplanets X, 118231C (1 September 2021); https://doi.org/10.1117/12.2594910
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KEYWORDS
Adaptive optics

Autoregressive models

Planets

Wavefront sensors

Adaptive control

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