随机并行梯度下降光束净化实验研究

利用自适应光学技术进行光束净化是高能激光系统中一项重要的研究内容.为实现光束净化系统的小型化和低成本,基于系统性能评价函数无模型最优化的波前畸变校正方法是适合的技术方案.就随机并行梯度下降(SPGD)最优化算法在光束净化系统中的应用展开研究.针对高能激光束常见的像差分布进行了SPGD波前校正的数值模拟,在此基础上构建了37单元自适应光学光束净化实验平台,讨论了双边扰动梯度估计和迭代增益系数自适应变化对算法收敛特性的影响.数值模拟与实验结果验证了SPGD算法对不同程度波前畸变的校正能力,表明了SPGD光束净化方案的可行性.

Experimental Explorations of the Laser Beam Cleanup System Based on Stochastic Parallel-Gradient-Descent Algorithm

Realizing beam cleanup using adaptive optics technique is an important research field of the high energy laser systems. To arrive at the aim of miniaturizing and low cost of the beam cleanup system, the method of wavefront distortion correction based on model-free optimization of the system performance metric is an appropriate scheme. This paper researches the application of the stochastic parallel gradient descent (SPGD) optimization algorithm on the beam cleanup system. Numerical simulations of the SPGD wavefront correction of phase aberrations commonly found in high energy laser beams were first carried out. Above this, an experimental 37-element adaptive optics beam cleanup system was set up and the influences of the two-sided perturbation method and the adaptive change of the iterative gain coefficient were studied on the convergence performance of the algorithm. The results of the numerical simulation and experiments verify the ability of the SPGD wavefront control method to correct different strengths of wavefront distortions and indicate the feasibility of the SPGD beam cleanup method.