自适应光学中SPGD算法关键参数实时调节方法

介绍了随机并行梯度下降（SPGD）算法及其在相干合成中的应用，针对实验中算法关键参数难以调节的难点，提出采用软硬件结合的新方式，实现对实验数据的在线采集和分析以及对SPGD算法关键参数的自动实时调节。开展了4路光纤激光相干合成实验，对不同调节方法进行对比。实验中采用新方式有效调节了SPGD算法中增益系数和随机扰动幅度的取值，合成效果显著。

Adjusting key parameters in SPGD algorithm for adaptive optics systems

The theory about stochastic parallel gradient descent(SPGD) algorithm and the use of SPGD algorithm for coherent beam combination are introduced. For solving the difficulty for adjusting the key parameters in SPGD algorithm, a new method of combining the hardware and software is proposed, which can online collect and analyze the experimental data and automatically adjust the key parameters in SPGD in real time. Experiments of 4 fiber-laser coherent beam combination are developed, in which different adjusting methods are compared. The results show that using new methods can efficiently achieve the value of the gain coefficient and the stochastic perturbation amplitude in SPGD algorithm. The experiments are profitable. The new method proposed in the paper is novel and effective, which can also guide the coherent beam combining experiments in the future.