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Proceedings Paper

Analysis of correction capability of 97-element deformable mirror based on SPGD
Author(s): Qi Xu; Haibo Yang; Yang Wu; Huizhen Yang
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Paper Abstract

Deformable mirror (DM) is a key component to the adaptive optics (AO) system. Its ability to fit the wavefront aberration determines the performance of the AO system. A wavefront sensorless adaptive optics (WFSless AO) system was used to investigate the fitting capability of a 97-element DM. We examined the fitting from the single order aberration of Zernike polynomial, combined aberration of Zernike polynomial and closed-loop correction, where the stochastic parallel gradient decent (SPGD) was used as close-loop correction algorithm. The results show that the 97-element DM has a good fitting ability to the first 3-54th single order of Zernike aberration. When the original wavefront RMS value was one wavelength, the residual wavefront aberration RMS was less than 0.4λ. The fitting to combine aberration of Zernike polynomial and closed-loop correction results based on the SPGD control algorithm show that the aberration was basically completely corrected when the wavefront aberration was small. When the wavefront aberration was relatively large, such as D/r0=20, the RMS of the residual wavefront aberration were smaller than 0.14λ (the initial RMS is 0.63λ). Above analysis results can provide theoretical basis and important reference for the practical application of the 97-element DM.

Paper Details

Date Published: 18 December 2019
PDF: 8 pages
Proc. SPIE 11338, AOPC 2019: Optical Sensing and Imaging Technology, 113383J (18 December 2019); doi: 10.1117/12.2548104
Show Author Affiliations
Qi Xu, China Univ. of Mining and Technology (China)
Haibo Yang, Science and Technology on Electro-Optical Information Security Control Lab. (China)
Yang Wu, Jiangsu Ocean Univ. (China)
Huizhen Yang, Jiangsu Ocean Univ. (China)
Jiangsu Marine Resources Development Research Institute (China)

Published in SPIE Proceedings Vol. 11338:
AOPC 2019: Optical Sensing and Imaging Technology
John E. Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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