随机并行梯度下降算法是一种极具应用潜力的自适应光学系统控制算法,具有不依赖波前传感器直接对系统性能指标进行优化的特点。基于32单元变形镜、CCD成像器件等建立自适应光学系统随机并行梯度下降控制算法实验平台。考察算法增益系数和扰动幅度对校正效果和收敛速度的影响,验证随机并行梯度下降算法的基本原理。实验结果表明参量选取合适的情况下,随机并行梯度下降控制算法对静态或慢变化的畸变波前具有较好的校正能力。根据实验结果分析了影响随机并行梯度下降算法校正速度的主要因素。

The stochastic parallel gradient descent algorithm (SPGD) is a promising control algorithm for adaptive-optics (AO) system, which is independent of wave-front sensor and can optimize the system performance directly. Based on SPGD, an adaptive-optics test-bed was built with a 32-element deformable mirror and a CCD. The principle of SPGD control algorithm was demonstrated through examining the effects of gain and perturbation amplitude on correction capability and convergence rate. Experimental results show SPGD can correct static distorted wave-front successfully when the gain and the perturbation amplitude were appropriate for distorted wave-front. The main effects on correction speed were discussed through analyzing experimental results.