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arXiv · 2608.24339

On-sky demonstration of self-learning predictive control with MagAO-X

Sebastiaan Y. Haffert·Jared R. Males·Parker T. Johnson·Laird M. Close·Olivier Guyon·Jay Kueny·Joshua Liberman·Joseph D. Long·Miles Lucas·Eden McEwen·Tiffany Nguyen·Adam K. Taras·Kyle Van Gorkom·Maggie Kautz·Katie Twitchell·Lauren Schatz
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Abstract

Direct imaging of exoplanets is very tricky and requires extremely well corrected wavefronts. Especially low-order order modes are detrimental to the performance of coronagraphs at their inner-working angle. However, that is precisely where conventional AO systems have the highest residuals that are caused by servo-lag errors. This servo-lag error can be reduced with predictive control where the control anticipates the future state of the atmospheric disturbance. We use a self-learning model predictive controller based on the concepts from sub-space predictive control (SPC). We present a novel implementation of the SPC by using an online QR-decomposition based recursive least squares approach. This approach has now been used for self-learning control of vibrations on the MagAO-X instrument. We see on average an Strehl increase of 15 percent and a decrease of the jitter to 0.9 mas. I will discuss how we have implemented the controller and its on-sky perfomance.

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Sebastiaan Y. Haffert, Jared R. Males, Parker T. Johnson, Laird M. Close, Olivier Guyon, Jay Kueny, Joshua Liberman, Joseph D. Long, Miles Lucas, Eden McEwen, Tiffany Nguyen, Adam K. Taras, Kyle Van Gorkom, Maggie Kautz, Katie Twitchell, Lauren Schatz. 2026-08-25. On-sky demonstration of self-learning predictive control with MagAO-X. https://doi.org/10.1117/12.3119213

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