在自适应光学中, 随机并行梯度下降(SPGD)算法通过对系统的性能指标直接优化从而校正波前像差, 具有很强的应用潜力。在点目标成像自适应光学系统中, SPGD算法经常采用强度分布平方和、平均半径和环围能量作为系统的性能指标进行优化。利用数值仿真分析了三种性能指标与畸变波前的均方根之间的关系。建立了一套实验平台, 通过静态波前畸变校正实验, 分析了SPGD算法采用以上三种不同的性能指标时的校正效果。实验结果与前面的数值仿真结果一致, 表明SPGD取平均半径作为性能指标进行优化时效果较好。实验还分析了控制通道数目对收敛速度的影响。结果表明随着控制通道数目的增加性能指标曲线收敛所需的迭代次数显著增加, 与驱动器个数的平方根之间存在一个近似的线性关系。

The stochastic parallel gradient descent (SPGD) algorithm can be used to correct phase distortion by optimizing the system performance index directly, so it is a promising control algorithm for adaptive optics (AO) system. In the point-source adaptive optics based on SPGD algorithm, the quadratic sum of intensity, the mean radius and encircled energy are usually used as the system performance index. The relation between the three performance index and the RMS value of aberrant wavefront are analyzed using numerical simulation. Then an adaptive optics experimental system is set up and the static phase-distortion correction experimental is carried out to research three performance index of SPGD algorithm. The experiment results show that the mean radius is the most efficient as the system performance index compared with the other two, which is accordant with the result of numerical simulation previously. The relation between the speed of convergence and control channel number is also researched. The experimental results show that the iteration amount required by convergence of performance index curve is increased remarkably when the control channel number is increased, and it is approximately proportional to square root of control channel number.