Chang Hui, Ge Wen-Qi, Wang Hao-Cheng, Yuan Hong, Fan Zhong-Wei
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2021 Mar 10;21(6):1946. doi: 10.3390/s21061946.
In laser systems, beam pointing usually drifts as a consequence of various disturbances, e.g., inherent drift, airflow, transmission medium variation, mechanical vibration, and elastic deformation. In this paper, we develop a laser beam pointing control system with Fast Steering Mirrors (FSMs) and Position Sensitive Devices (PSDs), which is capable of stabilizing both the position and angle of a laser beam. Specifically, using the ABCD matrix, we analyze the kinematic model governing the relationship between the rotation angles of two FSMs and the four degree-of-freedom (DOF) beam vector. Then, we design a Jacobian matrix feedback controller, which can be conveniently calibrated. Since disturbances vary significantly in terms of inconsistent physical characteristics and temporal patterns, great challenges are imposed to control strategies. In order to improve beam pointing control performance under a variety of disturbances, we propose a data-driven disturbance classification method by using a Recurrent Neural Network (RNN). The trained RNN model can classify the disturbance type in real time, and the corresponding type can be subsequently used to select suitable control parameters. This approach can realize the universality of the beam stabilization pointing system under various disturbances. Experiments on beam pointing control under several typical external disturbances are carried out to verify the effectiveness of the proposed control system.
在激光系统中，由于各种干扰，例如固有漂移、气流、传输介质变化、机械振动和弹性变形，光束指向通常会发生漂移。在本文中，我们开发了一种带有快速转向镜（FSM）和位置敏感探测器（PSD）的激光光束指向控制系统，该系统能够稳定激光束的位置和角度。具体而言，我们使用ABCD矩阵分析了控制两个FSM旋转角度与四自由度（DOF）光束矢量之间关系的运动学模型。然后，我们设计了一种可方便校准的雅可比矩阵反馈控制器。由于干扰在物理特性和时间模式上存在显著差异，给控制策略带来了巨大挑战。为了在各种干扰下提高光束指向控制性能，我们提出了一种使用递归神经网络（RNN）的数据驱动干扰分类方法。经过训练的RNN模型可以实时对干扰类型进行分类，随后可根据相应类型选择合适的控制参数。这种方法可以实现光束稳定指向系统在各种干扰下的通用性。我们进行了几种典型外部干扰下的光束指向控制实验，以验证所提出控制系统的有效性。
