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Abstract：
Modal-free optimization algorithms do not require specific mathematical models, and they, along with their other benefits, have great application potential in adaptive optics. In this study, two different algorithms, the single-dimensional perturbation descent algorithm (SDPD) and the second-order stochastic parallel gradient descent algorithm (2SPGD), are proposed for wavefront sensorless adaptive optics, and a theoretical analysis of the algorithms\' convergence rates is presented. The results demonstrate that the single-dimensional perturbation descent algorithm outperforms the stochastic parallel gradient descent (SPGD) and 2SPGD algorithms in terms of convergence speed. Then, a 32-unit deformable mirror is constructed as the wavefront corrector, and the SPGD, single-dimensional perturbation descent, and 2SPSA algorithms are used in an adaptive optics numerical simulation model of the wavefront controller. Similarly, a 39-unit deformable mirror is constructed as the wavefront controller, and the SPGD and single-dimensional perturbation descent algorithms are used in an adaptive optics experimental verification device of the wavefront controller. The outcomes demonstrate that the convergence speed of the algorithm developed in this paper is more than twice as fast as that of the SPGD and 2SPGD algorithms, and the convergence accuracy of the algorithm is 4% better than that of the SPGD algorithm.
摘要：
无模态优化算法不需要特定的数学模型,而他们,以及他们的其他好处，在自适应光学领域具有巨大的应用潜力。在这项研究中,两种不同的算法，单维扰动下降算法(SDPD)和二阶随机并行梯度下降算法(2SPGD),提出了无波前传感器自适应光学，并对算法的收敛速度进行了理论分析。结果表明，单维扰动下降算法在收敛速度方面优于随机并行梯度下降算法（SPGD）和2SPGD算法。然后，32单元的可变形镜被构造为波前校正器，还有SPGD,一维扰动下降，和2SPSA算法用于波前控制器的自适应光学数值仿真模型。同样，一个39单元的可变形镜被构造为波前控制器，并将SPGD和单维扰动下降算法应用于波前控制器的自适应光学实验验证装置中。结果表明，本文开发的算法的收敛速度是SPGD和2SPGD算法的两倍多，算法的收敛精度比SPGD算法好4%。
