Measurement method of the misalignment between the Hartmann–Shack sensor and the deformable mirror in an adaptive optics system

Gu, Naiting; Yang, Zhongmin; Huang, Linhai; Rao, Changhui

doi:10.1088/2040-8978/12/9/095504

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…The Shack-Hartmann wavefront sensor is commonly used in adaptive optics systems [30][31][32][33]. It consists of an array of lenses (each unit called lenslet) of the same focal length, as shown in Fig.…”

Section: Shack-hartmann Wavefront Sensormentioning

confidence: 99%

Optical Wavefront Detection: A Beginner Tutorial

Zheng¹,

Ibrahim²,

Ng³

et al. 2021

Digital Encyclopedia of Applied Physics

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This article is designed to help readers understand some typical wavefront detection techniques and gain a preliminary understanding of wavefront detection. In this tutorial, the wavefront definition used in wavefront technology is given and we divide wavefront detection technologies into qualitative and quantitative categories. In each category, we discuss some typical systems and provide simulation results. This tutorial comes with Matlab simulation codes that readers can use to simulate mentioned wavefront detection technologies on their computers. We hope that this tutorial provides readers with all the tools needed to understand and simulate the different wavefront technologies and modify them based on their needs and interest.

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Section: Shack-hartmann Wavefront Sensormentioning

confidence: 99%

Optical Wavefront Detection: A Beginner Tutorial

Zheng¹,

Ibrahim²,

Ng³

et al. 2021

Digital Encyclopedia of Applied Physics

View full text Add to dashboard Cite

show abstract

“…However, traditional PI control strictly depends on the response matrix of the deformable mirror, and the response matrix of the deformable mirror needs to be calibrated [5]. The alignment error between the HS wavefront sensor and the deformable mirror is caused by various factors in AO systems, so the model calibrated at a certain time may not be as accurate as when it is calibrated at another time [6,7]. In this case, the stability of the system will be greatly affected if there is no parameter self-tuning using good adaptive algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the alignment error will lead to a large error in the response matrix. Because the PI controller strictly relies on the response matrix B, this directly affects the performance of the PI controller [7,13,14].…”

mentioning

confidence: 99%

Deep learning control model for adaptive optics systems

Yang

et al. 2019

Appl. Opt.

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To correct wavefront aberrations, commonly employing proportional-integral control in adaptive optics (AO) systems, the control process depends strictly on the response matrix of the deformable mirror. The alignment error between the Hartmann-Shack wavefront sensor and the deformable mirror is caused by various factors in AO systems. In the conventional control method, the response matrix can be recalibrated to reduce the impact of alignment error, but the impact cannot be eliminated. This paper proposes a control method based on a deep learning control model (DLCM) to compensate for wavefront aberrations, eliminating the dependence on the deformable mirror response matrix. Based on the wavefront slope data, the cost functions of the model network and the actor network are defined, and the gradient optimization algorithm improves the efficiency of the network training. The model network guarantees the stability and convergence speed, while the actor network improves the control accuracy, realizing an online identification and self-adaptive control of the system. A parameter-sharing mechanism is adopted between the model network and the actor network to control the system gain. Simulation results show that the DLCM has good adaptability and stability. Through self-learning, it improves the convergence accuracy and iterations, as well as the adjustment tolerance of the system. of the deformable mirror relative to the center of the HS detector, as well as the rotation error α of the deformable mirror. Alignment error will change the design-matching relationship between the deformable mirror and HS sensor. Therefore, the alignment error will lead to a large error in the response matrix. Because the PI controller strictly relies on the response matrix B, this directly affects the performance of the PI controller [7,13,14]. IMPLEMENTATION PRINCIPLE OF THE DLCMThe DLCM consists of model network M S, V e jθ m , actor network AS, V jθ a , decision sample space R, and cost functions (J and J 0 ), as defined in Eqs. (4) and (9). The model network and the actor network have different roles. The model network has three functions: (1) shares the parameters with the actor network; (2) stabilizes the output of the actor network;(3) improves the convergence speed of the actor network. The actor network has two functions: (1) updates the decision sample space and guides the update of the model network;(2) improves control accuracy of the DLCM. The decision sample space is a queue structure where the optimal and up-to-date decision samples are stored. The cost function J is used to train the model network and learn the control rules from the decision samples. The cost function J 0 is used to update the actor network and the model network in real time. The calculation of the cost function J 0 is the key to the system. A. Model Network

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Measurement method of the misalignment between the Hartmann–Shack sensor and the deformable mirror in an adaptive optics system

Cited by 2 publications

References 15 publications

Optical Wavefront Detection: A Beginner Tutorial

Optical Wavefront Detection: A Beginner Tutorial

Deep learning control model for adaptive optics systems

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