Adaptive Gradient Estimation Stochastic Parallel Gradient Descent Algorithm for Laser Beam Cleanup

被引：7

作者：

Ma, Shiqing ^{[1
,2
,3
]}

Yang, Ping ^{[1
,2
]}

Lai, Boheng ^{[1
,2
]}

Su, Chunxuan ^{[1
,2
,3
]}

Zhao, Wang ^{[1
,2
]}

Yang, Kangjian ^{[1
,2
]}

Jin, Ruiyan ^{[1
,2
]}

Cheng, Tao ^{[1
,2
]}

Xu, Bing ^{[1
,2
]}

机构：

[1] Chinese Acad Sci, Key Lab Adapt Opt, Chengdu 610209, Peoples R China

[2] Chinese Acad Sci, Inst Opt & Elect, Chengdu 610209, Peoples R China

[3] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China

来源：

PHOTONICS | 2021年 / 8卷 / 05期

关键词：

stochastic parallel gradient descent algorithm; beam cleanup; slab laser;

D O I：

10.3390/photonics8050165

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

For a high-power slab solid-state laser, obtaining high output power and high output beam quality are the most important indicators. Adaptive optics systems can significantly improve beam qualities by compensating for the phase distortions of the laser beams. In this paper, we developed an improved algorithm called Adaptive Gradient Estimation Stochastic Parallel Gradient Descent (AGESPGD) algorithm for beam cleanup of a solid-state laser. A second-order gradient of the search point was introduced to modify the gradient estimation, and it was introduced with the adaptive gain coefficient method into the classical Stochastic Parallel Gradient Descent (SPGD) algorithm. The improved algorithm accelerates the search for convergence and prevents it from falling into a local extremum. Simulation and experimental results show that this method reduces the number of iterations by 40%, and the algorithm stability is also improved compared with the original SPGD method.

引用

页数：12

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