Open AccessMulti-perturbation stochastic parallel gradient descent method for wavefront correction
Author(s) -
Kenan Wu,
Yang Sun,
Ying Huai,
Shuqin Jia,
Xi Chen,
Yuqi Jin
Publication year - 2015
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.23.002933
Subject(s) - wavefront , gradient descent , adaptive optics , modal , optics , perturbation (astronomy) , strehl ratio , computer science , control theory (sociology) , deformable mirror , stochastic gradient descent , physics , materials science , artificial neural network , control (management) , quantum mechanics , machine learning , artificial intelligence , polymer chemistry
The multi-perturbation stochastic parallel gradient descent (SPGD) method for adaptive optics is presented in this work. The method is based on a new architecture. The incoming beam with distorted wavefront is split into N sub-beams. Each sub-beam is modulated by a wavefront corrector and its performance metric is measured subsequently. Adaptive system based on the multi-perturbation SPGD can operate in two modes - the fast descent mode and the modal basis updating mode. Control methods of the two operation modes are given. Experiments were carried out to prove the effectiveness of the proposed method. Analysis as well as experimental results showed that the two operation modes of the multi-perturbation SPGD enhance the conventional SPGD in different ways. The fast descent mode provides faster convergence than the conventional SPGD. The modal basis updating mode can optimize the modal basis set for SPGD with global coupling.