机器学习在光纤激光阵列相位调控中的研究进展与发展趋势  

Research Progress and Development Trend of Machine Learning in Phase Control of Fiber Laser Arrays

作　　者：高志强 常琦 刘昊宇 李俊 马鹏飞 周朴[1] Gao Zhiqaing;Chang Qi;Liu Haoyu;Li Jun;Ma Pengfei;Zhou Pu(College of Advanced Interdisciplinary Studies,National University of Defense Technology,Changsha 410073,Hunan,China;Nanhu Laser Laboratory,National University of Defense Technology,Changsha 410073,Hunan,China;Hunan Provincial Key Laboratory of High Energy Laser Technology,Changsha 410073,Hunan,China)

机构地区：[1]国防科技大学前沿交叉学科学院,湖南长沙410073 [2]国防科技大学南湖之光实验室,湖南长沙410073 [3]高能激光技术湖南省重点实验室,湖南长沙410073

出　　处：《中国激光》2023年第11期147-161,共15页Chinese Journal of Lasers

基　　金：国家自然科学基金(62075242)。

摘　　要：光纤激光阵列相位调控技术既可以突破单路光纤激光功率提升瓶颈,也是高功率特殊光场生成的有效途径之一。随着人工智能技术的迅速发展,将先进的智能算法引入激光阵列系统的控制模块中有望实现闭环相位控制能力的提升。综述了近年来基于机器学习的光纤激光阵列相位控制技术的最新研究进展,并对机器学习赋能光纤激光阵列相位调控的发展趋势和挑战进行了展望。Significance Phase control is a key factor in achieving coherent beam combining.Recently,the number of coherent combining paths has been continuously expanding,and the achieved combining power has been continuously increasing.However,when the power of a single combining light source exceeds kilowatts or even several kilowatts,the residual of the phase-locked control system significantly increases with the complexity of the application environment.With the rapid development of artificial intelligence technology,exploring new phase control methods based on machine learning has become a new development trend.Progress In 2019,Tünnermann et al.introduced reinforcement learning into coherent combining systems,achieving the prediction and compensation of phase noise below kHz(Fig.1).In 2021,the team validated the feasibility of applying the reinforcement learning phase-locked control method to tiled-aperture coherent combining systems in a simulation environment and explored the ability of the control method to achieve combining light field shaping(Fig.2).To overcome the limitations of reinforcement learning in expanding the number of coherent combining units,in 2021,Shpakovych et al.proposed a two-dimensional phase dynamic control scheme based on neural networks.This scheme uses a quasi-reinforcement learning method based on neural networks,and the phase-locked residual can reach up toλ/30(Fig.3).In 2022,Shpakovych et al.implemented the phase control of a seven-channel fiber amplifier array using a quasi-reinforcement learning algorithm(Fig.4).To test the feasibility of phase locking using deep learning in energy-type fiber laser coherent combining systems,in 2019,Hou et al.introduced deep learning into coherent combining systems for the first time and achieved phase locking(Fig.6).Subsequently,the Chinese Academy of Sciences in China,the Berkeley National Laboratory in the United States,and the University of Southampton in the United Kingdom conducted the concept or experimental verification of phase-locking based o

关 键 词：激光光学 光纤激光 阵列激光 机器学习 相位控制 

分 类 号：O436[机械工程—光学工程]