Chinese Journal of Lasers, Volume. 47, Issue 1, 0105001(2020)

Self-Learning Control Model for Adaptive Optics Systems and Experimental Verification

Zhenxing Xu1,2,3,4、**, Ping Yang1,3,4、*, Tao Cheng1,3,4, Bing Xu1,3,4, and Heping Li2
Author Affiliations
  • 1Key Laboratory on Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Sciences,Chengdu, Sichuan 610209, China
  • 2School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China,Chengdu, Sichuan 610054, China
  • 3Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
  • 4University of Chinese Academy of Sciences, Beijing 100039, China
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    In adaptive optics systems, the traditional proportional-integral control model relies on the response matrix of the deformable mirror, which is sensitive to changes in the system state. When the response matrix is altered, the wavefront correction performance is degraded. In this paper, the output of control signal from Hartman slope data is realized by redefining the back-propagation neural network structure, and a control model is established. Experimental results show that the proposed model eliminates the limitation of the traditional fixed model and acquires the characteristics of an online real-time update response model. The control model delivers high convergence performance, can adapt to environmental changes, and is robust. It also improves the control precision and the control performance to a certain extent.

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    Zhenxing Xu, Ping Yang, Tao Cheng, Bing Xu, Heping Li. Self-Learning Control Model for Adaptive Optics Systems and Experimental Verification[J]. Chinese Journal of Lasers, 2020, 47(1): 0105001

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    Paper Information

    Category: beam transmission and control

    Received: Jun. 26, 2019

    Accepted: Sep. 26, 2019

    Published Online: Jan. 9, 2020

    The Author Email: Zhenxing Xu (xyhf2009@foxmail.com), Ping Yang (pingyang2516@163.com)

    DOI:10.3788/CJL202047.0105001

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