用于高对比度成像的自适应光学数据驱动子空间预测控制
Data-driven subspace predictive control of adaptive optics for high-contrast imaging
摘要：
对系外行星的搜寻正将地面望远镜上的自适应光学系统推向极限。在小角度分离的主要限制之一，正是预测系外行星，是伺服滞后的自适应光学系统。伺服滞后误差可以用预测控制来减小，其中控制是基于大气扰动的未来状态。我们建议使用一个线性数据驱动的积分预测控制器的子空间方法的基础上，实时更新。新的控制器只使用测量的波前误差和变形镜命令的变化，这允许闭环操作，而不需要伪开环重构。这使得能够与诸如棱锥波前传感器的非线性波前传感器一起操作。我们表明，所提出的控制器进行近最优控制在模拟平稳和非平稳干扰，我们能够获得几个数量级的原始对比度。该算法已在实验室中使用MagAO-X进行了演示，我们获得了超过两个数量级的对比度。
英文摘要：
The search for exoplanets is pushing adaptive optics 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 adaptive optics 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 is 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.
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