Xu Zhenxing, Yang Ping, Hu Ke, Xu Bing, Li Heping
Appl Opt. 2019 Mar 10;58(8):1998-2009. doi: 10.1364/AO.58.001998.
To correct wavefront aberrations, commonly employing proportional-integral control in adaptive optics (AO) systems, the control process depends strictly on the response matrix of the deformable mirror. The alignment error between the Hartmann-Shack wavefront sensor and the deformable mirror is caused by various factors in AO systems. In the conventional control method, the response matrix can be recalibrated to reduce the impact of alignment error, but the impact cannot be eliminated. This paper proposes a control method based on a deep learning control model (DLCM) to compensate for wavefront aberrations, eliminating the dependence on the deformable mirror response matrix. Based on the wavefront slope data, the cost functions of the model network and the actor network are defined, and the gradient optimization algorithm improves the efficiency of the network training. The model network guarantees the stability and convergence speed, while the actor network improves the control accuracy, realizing an online identification and self-adaptive control of the system. A parameter-sharing mechanism is adopted between the model network and the actor network to control the system gain. Simulation results show that the DLCM has good adaptability and stability. Through self-learning, it improves the convergence accuracy and iterations, as well as the adjustment tolerance of the system.
为了校正波前像差，自适应光学（AO）系统通常采用比例积分控制，控制过程严格依赖于可变形镜的响应矩阵。哈特曼-夏克波前传感器与可变形镜之间的对准误差是由AO系统中的各种因素引起的。在传统控制方法中，可以重新校准响应矩阵以减少对准误差的影响，但这种影响无法消除。本文提出了一种基于深度学习控制模型（DLCM）的控制方法来补偿波前像差，消除了对可变形镜响应矩阵的依赖。基于波前斜率数据，定义了模型网络和动作网络的代价函数，并采用梯度优化算法提高了网络训练效率。模型网络保证了稳定性和收敛速度，而动作网络提高了控制精度，实现了系统的在线识别和自适应控制。模型网络和动作网络之间采用参数共享机制来控制系统增益。仿真结果表明，DLCM具有良好的适应性和稳定性。通过自学习，它提高了收敛精度和迭代次数，以及系统的调整容限。
