发表机构
Université Lyon 1; CNRS UMR 5574; Ecole Normale Supérieure de Lyon（里昂第一大学; 法国国家科学研究中心第5574联合研究实验室；里昂高等师范学校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对欧洲极大望远镜等的自适应光学系统，研究闭环观测中无额外扰动的系统参数辨识问题，推导方程并分析算法，介绍两种实现方式以优化观测中的AO校正。
AI 中文摘要
欧洲极大望远镜（E-ELT）的自适应光学（AO）系统，以及2014年甚大望远镜（VLT）上的自适应光学设施等早期探路者系统，将不再是固定系统。AO不再孤立于试验台，部分元件直接处于望远镜的光学链路中，在观测过程中会受到环境及约束变化的影响。为保证任意观测时刻的良好性能，本文研究了一种自校准策略，重点关注最具挑战性的方面之一：在闭环观测中辨识系统参数且不引入任何额外扰动。该问题在辨识理论中被认为难以解决。Béchet等人（2011年AO4ELT2会议）曾提出一种针对该问题的辨识方法，在模拟中获得了良好结果。为巩固这些进展，本文重新推导相关方程并提供理论分析以论证算法选择的合理性，强调使用增量数据和指令去相关扰动的益处，同时介绍了欧洲南方天文台目前研究的该方法的两种实现方式。
英文摘要
The adaptive optics (AO) on the European Extremely Large Telescope, as well as earlier pathfinders like the Adaptive Optics Facility, at the Very Large Telescope in 2014, will no longer be stationary systems. AO is no longer isolated on a bench; some elements are directly in the optical train of the telescope, suffering environment and constrains changes during the observations. To guarantee good performance at any observing time, we investigate a self-calibration strategy. We focus here on one of the most challenging aspects: the identification of system parameters during closed-loop observations without introducing any additional disturbance. Such problem is known in the identification theory to be difficult to solve. We have recently presented (Béchet et al., AO4ELT2 Conference, 2011) an identification method for this, with promising results obtained in simulations. To consolidate these advances, we come back in the present paper to the equations and provide a theoretical analysis to justify the choice of the algorithm. We highlight the benefit of using incremental data and commands to decorrelate the disturbance. We also present 2 implementations of the method, currently studied at the European Southern Observatory.
Journal refC. Béchet, M. Tallon, É. Thiébaut "Optimization of adaptive optics correction during observations: algorithms and system parameters identification in closed-loop", Proc. SPIE 8447, Adaptive Optics Systems III, 84472C (2012)
