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.

中文翻译：

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高对比度成像的自适应光学的数据驱动子空间预测控制

寻找系外行星的方法将地面望远镜上的自适应光学（AO）系统推向了极限。在小角度间隔（准确地说是系外行星的位置）上的主要限制之一是AO系统的伺服滞后。可以通过预测控制来减少伺服滞后误差，其中预测控制基于大气干扰的未来状态。我们建议使用基于实时更新的子空间方法的线性数据驱动的积分预测控制器。新的控制器仅使用测得的波前误差和可变形镜像命令的变化，从而允许闭环操作，而无需伪开环重构。这使得能够使用非线性波前传感器（例如金字塔波前传感器）进行操作。我们表明，所提出的控制器在仿真中对稳态和非稳态扰动都执行了近乎最优的控制，并且我们能够在原始对比度中获得几个数量级。该算法已在实验室用MagAO-X进行了演示，相比之下，我们获得了两个数量级以上的对比。