Adaptive Gradient Estimation Stochastic Parallel Gradient Descent Algorithm for Laser Beam Cleanup
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/8/5/165 |
| _version_ | 1827692137825697792 |
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| author | Shiqing Ma Ping Yang Boheng Lai Chunxuan Su Wang Zhao Kangjian Yang Ruiyan Jin Tao Cheng Bing Xu |
| author_facet | Shiqing Ma Ping Yang Boheng Lai Chunxuan Su Wang Zhao Kangjian Yang Ruiyan Jin Tao Cheng Bing Xu |
| author_sort | Shiqing Ma |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:17:08Z |
| format | Article |
| id | doaj.art-7b951413ac11410cb4cce21e2aefc319 |
| institution | Directory Open Access Journal |
| issn | 2304-6732 |
| language | English |
| last_indexed | 2024-03-10T11:17:08Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj.art-7b951413ac11410cb4cce21e2aefc3192023-11-21T20:21:40ZengMDPI AGPhotonics2304-67322021-05-018516510.3390/photonics8050165Adaptive Gradient Estimation Stochastic Parallel Gradient Descent Algorithm for Laser Beam CleanupShiqing Ma0Ping Yang1Boheng Lai2Chunxuan Su3Wang Zhao4Kangjian Yang5Ruiyan Jin6Tao Cheng7Bing Xu8Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, ChinaKey Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, ChinaKey Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, ChinaKey Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, ChinaKey Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, ChinaKey Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, ChinaKey Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, ChinaKey Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, ChinaKey Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, ChinaFor 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.https://www.mdpi.com/2304-6732/8/5/165stochastic parallel gradient descent algorithmbeam cleanupslab laser |
| spellingShingle | Shiqing Ma Ping Yang Boheng Lai Chunxuan Su Wang Zhao Kangjian Yang Ruiyan Jin Tao Cheng Bing Xu Adaptive Gradient Estimation Stochastic Parallel Gradient Descent Algorithm for Laser Beam Cleanup Photonics stochastic parallel gradient descent algorithm beam cleanup slab laser |
| title | Adaptive Gradient Estimation Stochastic Parallel Gradient Descent Algorithm for Laser Beam Cleanup |
| title_full | Adaptive Gradient Estimation Stochastic Parallel Gradient Descent Algorithm for Laser Beam Cleanup |
| title_fullStr | Adaptive Gradient Estimation Stochastic Parallel Gradient Descent Algorithm for Laser Beam Cleanup |
| title_full_unstemmed | Adaptive Gradient Estimation Stochastic Parallel Gradient Descent Algorithm for Laser Beam Cleanup |
| title_short | Adaptive Gradient Estimation Stochastic Parallel Gradient Descent Algorithm for Laser Beam Cleanup |
| title_sort | adaptive gradient estimation stochastic parallel gradient descent algorithm for laser beam cleanup |
| topic | stochastic parallel gradient descent algorithm beam cleanup slab laser |
| url | https://www.mdpi.com/2304-6732/8/5/165 |
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