论文标题
通过增强学习的自适应自适应光学控制
Self-optimizing adaptive optics control with Reinforcement Learning
论文作者
论文摘要
当前和未来的高对比度成像仪器需要极端的自适应光学器件（XAO）系统才能达到直接成像系外行星所需的对比度。控制回路的潜伏期限制了这些系统的性能引起的望远镜振动和时间误差。 （预测）对照算法的优化对于降低这些影响至关重要。我们描述了如何使用无模型的增强学习来优化闭环自适应光学控制器的复发性神经网络控制器。我们验证了在模拟和实验室设置中进行倾斜控制的建议方法。结果表明，该算法可以有效地学会抑制倾斜振动的组合。此外，与最佳增益积分器相比，我们报告了幂律输入湍流的残差减少。最后，我们证明控制器可以学会识别不同振动的参数，而无需在线更新控制法。我们得出的结论是，强化学习是针对数据驱动的预测控制的一种有前途的方法。未来的研究将把这种方法应用于高阶可变形镜的控制
Current and future high-contrast imaging instruments require extreme Adaptive Optics (XAO) systems to reach contrasts necessary to directly image exoplanets. Telescope vibrations and the temporal error induced by the latency of the control loop limit the performance of these systems. Optimization of the (predictive) control algorithm is crucial in reducing these effects. We describe how model-free Reinforcement Learning can be used to optimize a Recurrent Neural Network controller for closed-loop adaptive optics control. We verify our proposed approach for tip-tilt control in simulations and a lab setup. The results show that this algorithm can effectively learn to suppress a combination of tip-tilt vibrations. Furthermore, we report decreased residuals for power-law input turbulence compared to an optimal gain integrator. Finally, we demonstrate that the controller can learn to identify the parameters of a varying vibration without requiring online updating of the control law. We conclude that Reinforcement Learning is a promising approach towards data-driven predictive control; future research will apply this approach to the control of high-order deformable mirrors
