Predictive optimization algorithm for beam combination systems based on adaptive fiber optics collimators

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.