中国激光, 2020, 47 (1): 0105001, 网络出版: 2020-01-09
自适应光学系统的自学习控制模型及其验证 下载: 1270次
Self-Learning Control Model for Adaptive Optics Systems and Experimental Verification
摘要
在自适应光学系统中,传统比例-积分控制模型依赖于变形镜的响应矩阵,系统状态的改变会对变形镜响应矩阵造成影响,导致波前校正性能下降。通过重新定义BP(back-propagation)神经网络结构实现哈特曼斜率数据到控制信号的输出,并建立了控制模型。实验结果表明,所提模型摆脱了传统固定模型的限制,具有在线更新控制模型的特点,控制模型收敛性能良好,能适应系统状态变化,有较强的鲁棒性,同时提高了控制精度,一定程度上改善了控制性能。
Abstract
In adaptive optics systems, the traditional proportional-integral control model relies on the response matrix of the deformable mirror, which is sensitive to changes in the system state. When the response matrix is altered, the wavefront correction performance is degraded. In this paper, the output of control signal from Hartman slope data is realized by redefining the back-propagation neural network structure, and a control model is established. Experimental results show that the proposed model eliminates the limitation of the traditional fixed model and acquires the characteristics of an online real-time update response model. The control model delivers high convergence performance, can adapt to environmental changes, and is robust. It also improves the control precision and the control performance to a certain extent.
许振兴, 杨平, 程涛, 许冰, 李和平. 自适应光学系统的自学习控制模型及其验证[J]. 中国激光, 2020, 47(1): 0105001. Zhenxing Xu, Ping Yang, Tao Cheng, Bing Xu, Heping Li. Self-Learning Control Model for Adaptive Optics Systems and Experimental Verification[J]. Chinese Journal of Lasers, 2020, 47(1): 0105001.