MagAO-X 自学习预测控制的在轨演示
On-sky demonstration of self-learning predictive control with MagAO-X
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中文总结 AI 辅助
针对系外行星直接成像的波前校正问题，采用基于子空间预测控制的自学习模型预测控制器，经在轨演示使MagAO-X的Strehl比提升15%、抖动降至0.9mas。
中文摘要 AI 辅助
系外行星直接成像极具挑战性，需要极高精度校正的波前，尤其是低阶模式会严重影响日冕仪在其内工作角处的性能，而这恰好是传统自适应光学（AO）系统因伺服延迟误差导致残差最大的区域。伺服延迟误差可通过预测控制减小，即控制过程可预测大气扰动的未来状态。我们采用基于子空间预测控制（SPC）概念的自学习模型预测控制器，提出一种新颖的SPC实现方式，使用基于在线QR分解的递归最小二乘方法。该方法已用于MagAO-X仪器的振动自学习控制，观测到平均Strehl比提升15%，抖动降至0.9毫角秒（mas）。本文将讨论控制器的实现方式及其在轨性能。
英文摘要
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.
