将一种改进的变增益系数自适应随机并行梯度下降(SPGD)控制算法应用到大阵列光纤激光相干合成中，计算不同增益系数对算法收敛速度的影响程度，分析判断该方法的控制带宽、控制时间与合成光束质量、合成路数的关系以及其应用于大规模阵列相干合成的可行性。计算结果显示，在7束光纤激光相干合成中，该方法由于采用了变增益系数的控制策略，相比于传统的固定增益系数SPGD 算法，具有收敛速度快、控制带宽高、适用于多组束光纤激光相干合成等优点。将该方法应用在37束、91束和100束光纤激光阵列锁相中，也得到了快速的收敛效果，采用自适应SPGD 算法分别将收敛速率提高了37.8%、63.8%和75.0%，说明该方法在合成路数较大时优势更加明显，进一步表明其具备向大阵列光束相干合成扩展的潜力。

An adaptive stochastic parallel gradient descent (SPGD) control algorithm with a variable gain coefficient is applied in coherent beam combining (CBC) of a large scale fiber laser array. The influence of different gain coefficients on convergence speed of the control algorithm is computed. The relationship between control bandwidth, iteration rates, beam quality of combination, laser numbers and the feasibility of a large scale coherent beam combining based on this adaptive algorithm is analyzed. The results show that in CBC of 7 fiber lasers, this adaptive SPGD algorithm using a variable gain coefficient control strategy holds advantage of high iteration rates, high control bandwidth and good applicability to coherent beam combining of fiber laser array. The fast convergence speed can also be obtained and the convergence speed is increased by 37.8%, 63.8% and 75.0% respectively, when this algorithm is applied in CBC of 37, 91 and 100 fiber lasers. We believe the proposed adaptive SPGD technique has the potential to be scaled to a large-scale array with high output power.