Chang Hui, Fan Zhongwei, Qiu Jisi, Ge Wenqi, Wang Haocheng, Yan Ying, Tang Xiongxin, Zhang Hongbo, Yuan Hong
Appl Opt. 2019 Feb 1;58(4):948-953. doi: 10.1364/AO.58.000948.
In laser systems, it is well known that beam pointing is shifted due to many un-modeled factors, such as vibrations from the hardware platform and air disturbance. In addition, beam-pointing shift also varies with laser sources as well as time, rendering the modeling of shifting errors difficult. While a few works have addressed the problem of predicting shift dynamics, several challenges still remain. Specifically, a generic approach that can be easily applied to different laser systems is highly desired. In contrast to physical modeling approaches, we aim to predict beam-pointing drift using a well-established probabilistic learning approach, i.e., the Gaussian mixture model. By exploiting sampled datapoints (collected from the laser system) comprising time and corresponding shifting errors, the joint distribution of time and shifting error can be estimated. Subsequently, Gaussian mixture regression is employed to predict the shifting error at any query time. The proposed learning scheme is verified in a pulsed laser system (1064 nm, Nd:YAG, 100 Hz), showing that the drift prediction approach achieves remarkable performances.
在激光系统中，众所周知，由于许多未建模的因素，如硬件平台的振动和空气扰动，光束指向会发生偏移。此外，光束指向偏移还会随激光源以及时间而变化，使得对偏移误差进行建模变得困难。虽然有一些工作已经解决了预测偏移动态的问题，但仍然存在几个挑战。具体而言，非常需要一种能够轻松应用于不同激光系统的通用方法。与物理建模方法不同，我们旨在使用一种成熟的概率学习方法，即高斯混合模型来预测光束指向漂移。通过利用包含时间和相应偏移误差的采样数据点（从激光系统收集），可以估计时间和偏移误差的联合分布。随后，采用高斯混合回归来预测任何查询时间的偏移误差。所提出的学习方案在脉冲激光系统（1064nm，Nd:YAG，100Hz）中得到了验证，表明漂移预测方法取得了显著的性能。
