Comparison of several stochastic parallel optimization algorithms for adaptive optics system without a wavefront sensor |
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| Authors: | Huizhen Yang Xinyang Li |
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| Institution: | 1. School of Electronic Engineering, Huaihai Institute of Technology, China;2. The Key Lab on Adaptive Optics, Chinese Academy of Sciences, China;1. Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China;2. Division of Engineering and Applied Science, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA;3. Key Laboratory of Opto-electronic Information Acquisition and Manipulation Ministry of Education, Anhui University, Hefei, 230601, China |
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| Abstract: | Optimizing the system performance metric directly is an important method for correcting wavefront aberrations in an adaptive optics (AO) system where wavefront sensing methods are unavailable or ineffective. An appropriate “Deformable Mirror” control algorithm is the key to successful wavefront correction. Based on several stochastic parallel optimization control algorithms, an adaptive optics system with a 61-element Deformable Mirror (DM) is simulated. Genetic Algorithm (GA), Stochastic Parallel Gradient Descent (SPGD), Simulated Annealing (SA) and Algorithm Of Pattern Extraction (Alopex) are compared in convergence speed and correction capability. The results show that all these algorithms have the ability to correct for atmospheric turbulence. Compared with least squares fitting, they almost obtain the best correction achievable for the 61-element DM. SA is the fastest and GA is the slowest in these algorithms. The number of perturbation by GA is almost 20 times larger than that of SA, 15 times larger than SPGD and 9 times larger than Alopex. |
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