提出了基于远场指标梯度的自适应光学闭环控制模型,该模型使用递归最小二乘来稳定响应矩阵,通过远场指标的梯度信息快速自学习当前的系统状态。结果表明:该模型具有在线实时更新的特点,能够自适应H-S子孔径缺光或质心探测不理想的状态,可在一定程度上改善控制性能。

In this study, we propose an adaptive optics closed-loop control model based on the far-field index gradient, which can be used to stabilize the response matrix based on the recursive least square values. Further, the current system state can be rapidly self-learned using the far-field index gradient. The experimental results denote that the proposed model exhibits real-time online update characteristics; furthermore, the proposed model can adapt to the state of H-S subaperture lack of light or non-ideal centroid detection, which improves the control performance to some extent.