Active correction of 1.23 m SiC mirror using bending mode

Yi, 朱 熠 Zhu; Tao, 陈 涛 Chen; Jian-li, 王建立 Wang; Hong-zhuang, 李宏壮 Li; Xiao-xia, 吴小霞 Wu

doi:10.3788/ope.20172510.2551

光学精密工程

2017

DOI: 10.3788/ope.20172510.2551

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Active correction of 1.23 m SiC mirror using bending mode

朱熠 Zhu Yi¹,

陈涛 Chen Tao²,

王建立 Wang Jian-li³

et al.

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Deep Learning Correction Algorithm for The Active Optics System

Chao

Guan

et al. 2020

Sensors

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The correction of wavefront aberration plays a vital role in active optics. The traditional correction algorithms based on the deformation of the mirror cannot effectively deal with disturbances in the real system. In this study, a new algorithm called deep learning correction algorithm (DLCA) is proposed to compensate for wavefront aberrations and improve the correction capability. The DLCA consists of an actor network and a strategy unit. The actor network is utilized to establish the mapping of active optics systems with disturbances and provide a search basis for the strategy unit, which can increase the search speed; The strategy unit is used to optimize the correction force, which can improve the accuracy of the DLCA. Notably, a heuristic search algorithm is applied to reduce the search time in the strategy unit. The simulation results show that the DLCA can effectively improve correction capability and has good adaptability. Compared with the least square algorithm (LSA), the algorithm we proposed has better performance, indicating that the DLCA is more accurate and can be used in active optics. Moreover, the proposed approach can provide a new idea for further research of active optics.

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Deep Learning Correction Algorithm for The Active Optics System

Chao

Guan

et al. 2020

Sensors

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show abstract

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Active correction of 1.23 m SiC mirror using bending mode

Cited by 1 publication

References 0 publications

Deep Learning Correction Algorithm for The Active Optics System

Deep Learning Correction Algorithm for The Active Optics System

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