激光相干合成系统中SPGD算法的分阶段自适应优化  

Staged adaptive optimization of SPGD algorithm in laser coherent beam combining

作　　者：郑文慧 祁家琴 江文隽 谭贵元 胡奇琪[5] 高怀恩[4] 豆嘉真 邸江磊 秦玉文[1,2,3] ZHENG Wenhui;QI Jiaqin;JIANG Wenjun;TAN Guiyuan;HU Qiqi;GAO Huaien;DOU Jiazhen;DI Jianglei;QIN Yuwen(Institute of Advanced Photonics Technology,School of Information Engineering,Guangdong University of Technology,Guangzhou 510006,China;Key Laboratory of Photonic Technology for Integrated Sensing and Communication,Ministry of Education,Guangzhou 510006,China;Guangdong Provincial Key Laboratory of Information Photonics Technology,Guangzhou 510006,China;School of Integrated Circuits,Guangdong University of Technology,Guangzhou 510006,China;Institute of Fluid Physics,CAEP,Mianyang 621900,China)

机构地区：[1]广东工业大学信息工程学院先进光子技术研究院,广东广州510006 [2]通感融合光子技术教育部重点实验室,广东广州510006 [3]广东省信息光子技术重点实验室,广东广州510006 [4]广东工业大学集成电路学院,广东广州510006 [5]中国工程物理研究院流体物理研究所,四川绵阳621900

出　　处：《红外与激光工程》2024年第9期303-315,共13页Infrared and Laser Engineering

基　　金：国家自然科学基金项目(62075183,62305072);广东省“珠江人才计划”引进创新创业团队项目(2021ZT09X044,2019ZT08X340)。

摘　　要：为改善传统随机并行梯度下降(Stochastic Parallel Gradient Descent,SPGD)算法应用于大规模激光相干合成系统时收敛速度慢且易陷入局部最优解的情况,提出了一种分阶段自适应增益SPGD算法-Staged SPGD算法。该算法根据性能评价函数值,在不同收敛时期采用不同策略对增益系数进行自适应调整,同时引入含梯度更新因子的控制电压更新策略,在加快收敛速度的同时减少算法陷入局部极值的概率。实验结果表明:在19路激光相干合成系统中,与传统SPGD算法相比,Staged SPGD算法的收敛速度提升了36.84%,针对不同频率和幅度的相位噪声,算法也具有较优的收敛效果,且稳定性得到显著提升。此外,将Staged SPGD算法直接应用于37、61、91路相干合成系统时,Staged SPGD算法相比传统SPGD算法收敛速度分别提升了37.88%、40.85%和41.10%,提升效果随相干合成单元数增加而更加显著,表明该算法在收敛速度、稳定性和扩展性方面均具有一定优势,具备扩展到大规模相干合成系统的潜力。Objective High-power lasers are being used in an increasingly wide range of applications.Coherent beam combining(CBC)of multiple lasers represents the most effective method for obtaining high-quality and high-power laser output.Fast and accurate phase control of each laser is crucial for achieving laser CBC,and the stochastic parallel gradient descent(SPGD)algorithm is widely used because of its advantages of parallel control and simple structure.However,the traditional SPGD algorithm faces challenges where the convergence speed and effectiveness are compromised due to fixed gain coefficients and disturbance amplitudes.Specifically,when the gain coefficient or disturbance amplitude is small,the algorithm tends to converge slowly.Conversely,setting a large gain coefficient or disturbance amplitude may cause the algorithm to fall into local optimal solutions.Moreover,as the number of composite beams increases,the required iteration steps for algorithm convergence will significantly rise,failing to meet the iteration time requirements for large-scale CBC systems.Therefore,optimizing the SPGD algorithm is essential to enhance its convergence speed,stability,and scalability.This optimization is a necessary step for realizing large-scale laser CBC.Methods In order to improve the issue that the traditional SPGD algorithm converges slowly and tends to fall into local optimal solutions when it is applied to large-scale laser CBC,a staged adaptive gain SPGD(Staged SPGD)algorithm is proposed.This algorithm adaptively adjusts the gain coefficient based on the performance evaluation function,using different strategies to enhance the convergence speed.Additionally,a control voltage update strategy with a gradient update factor is introduced to effectively update the control voltage,accelerate convergence,and mitigate instances where the algorithm gets stuck in local extreme values.Results and Discussions The performance of the traditional SPGD algorithm and the proposed algorithm in the 19-laser CBC system is analyzed.The resu

关 键 词：激光相干合成 相位控制 随机并行梯度下降算法 SPGD算法 

分 类 号：TN249[电子电信—物理电子学]