Predictive optimization algorithm for beam combination systems based on adaptive fiber optics collimators. (January 2022)

• Record Type: Journal Article
• Authors: Huang, Guan; Lv, Guoyun; Fan, Yangyu; Geng, Chao; Li, Xinyang
• Is Part Of: Optics and lasers in engineering. Volume 148(2022)
• Publication Date: 2022-01
• Subjects: Laser beam combining -- Optimization algorithm -- Model predictive control -- Target occlusion -- Adaptive fiber optics collimator; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2021.106753
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: Basic principle and the challenges of the beam combination systems based on adaptive fiber optics collimators are introduced. Impacts of the target occlusion on the performance of the beam combination systems are analyzed in detail. A predictive optimization algorithm is designed, which can monitor the target occlusion in real-time, and then keep the combined beams from divergence. The proposed method is validated by the laser projection experiment conducted in the free space with a distance of 40 m. Abstract: Beam combining of fiber lasers based on adaptive fiber optics collimator (AFOC) plays an important role in the laser propagation applications, whereas the system performance is severely affected by the target occlusion during the beam's long-range atmosphere transmission. In this paper, the sources, characteristics, and potential impacts of the target occlusion were analyzed for the first time, and the predictive stochastic parallel gradient descent (P-SPGD) algorithm was proposed to solve this problem. The basic principle of P-SPGD was to monitor the target occlusion in real-time, and then dynamically choose whether to use the observed gradient or the predicted gradient to update the control voltages of AFOCs. Beam combining experiments based on three AFOCs were conducted in the free space with a distance of 40 m. The results showed that when the moving target was obscured for 3 s, the AFOCs based on the traditional optimization algorithm had lost track of the target, whereas the P-SPGD algorithm can detect this emergency with a probability of 98.8%, and accurately model the motion state of the target. As a result, the AFOCs can still achieve stable target tracking during the period of target occlusion, and keep the combined beams from divergence.