自适应光学系统的自学习控制模型及其验证

In an adaptive optics system, the traditional proportional-integral control model depends on the response matrix of the deformable mirror. The change in the system state will affect the response matrix of the deformable mirror, resulting in a decline in wavefront correction performance. By redefining the back-propagation (BP) neural network structure, the output from Hartmann slope data to control signals is achieved, and a control model is established. The experimental results show that the proposed model breaks away from the limitations of the traditional fixed model, has the characteristic of online updating of the control model, has good convergence performance of the control model, can adapt to changes in the system state, has strong robustness, and at the same time improves the control accuracy and to some extent improves the control performance.

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。