.png){kind=icon}
返回
Adaptive optics control using model-based reinforcement learning
.png){kind=icon}
OA
AI
DOI：10.1364/OE.420270.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Reinforcement learning (RL) presents a new approach for controlling adaptive optics (AO) systems for Astronomy. It promises to effectively cope with some aspects often hampering AO performance such as temporal delay or calibration errors. We formulate the AO control loop as a model-based RL problem (MBRL) and apply it in numerical simulations to a simple Shack-Hartmann Sensor (SHS) based AO system with 24 resolution elements across the aperture. The simulations show that MBRL controlled AO predicts the temporal evolution of turbulence and adjusts to mis-registration between deformable mirror and SHS which is a typical calibration issue in AO. The method learns continuously on timescales of some seconds and is therefore capable of automatically adjusting to changing conditions. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Keyword:
NEURAL-NETWORKS
RECONSTRUCTION
SYSTEM
PREDICTION
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
机构
引用论文
Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems.png)
SENSORS
OAAI
SENSORSIF3.5
OAAI分享收藏分享收藏分享收藏
查看更多内容