Adaptive mode optimization of a continuous-wave solid-state laser using an intracavity piezoelectric deformable mirror

Liu, Yuan; Yang, Wei; Ao, Mingwu; Hu, Shijie; Xu, Bing; Jiang, Wenhan

doi:10.1016/j.optcom.2007.06.043

Cited by 28 publications

(12 citation statements)

References 5 publications

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“…The accuracy of ∇ V J 0 directly affects the convergence performance of the system and is the core factor that determines the system performance. There are many mature methods for gradient estimation in AO systems, from the early hill-climbing method [16] and the multivariate high-frequency vibration method [17,18] to the genetic algorithm [19,20], the ant colony algorithm [21], the simulated annealing algorithm [22], and the stochastic parallel gradient descent (SPGD) algorithm [23] in the past decade. SPGD is used to calculate the gradient ∇ V J 0 .…”

Section: Calculation Of Gradientmentioning

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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Section: Calculation Of Gradientmentioning

confidence: 99%

Deep learning control model for adaptive optics systems

Yang

et al. 2019

Appl. Opt.

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“…In addition, the damage threshold of micromachined components was comparatively low (20 W∕cm 2 ) [9], which limited its use in correction of high-power laser beams. Considering power tolerance and enough stroke, thin plate DMs driven by piezoceramics are normally preferred in the correction of high-power laser beams [10,11]. This paper proposes a DM with a multireflection waveguide, in which passable resolution and adequate stroke are achieved simultaneously within a small aperture.…”

Section: Introductionmentioning

confidence: 99%

Wavefront correction performed by a deformable mirror of arbitrary actuator pattern within a multireflection waveguide

Huang

Bian

et al. 2014

Appl. Opt.

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The wavefront correction ability of a deformable mirror with a multireflection waveguide was investigated and compared via simulations. By dividing a conventional actuator array into a multireflection waveguide that consisted of single-actuator units, an arbitrary actuator pattern could be achieved. A stochastic parallel perturbation algorithm was proposed to find the optimal actuator pattern for a particular aberration. Compared with conventional an actuator array, the multireflection waveguide showed significant advantages in correction of higher order aberrations.

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“…Controlling transverse modes in a laser cavity is a difficult task experimentally, usually employing custom optical elements [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] in the form of diffractive and/or aspheric optics, adaptive mirrors or graded phase mirrors, or specialized apertures. Good beam quality associated with lower-order modes is a fundamental requirement for most laser applications.…”

Section: Introductionmentioning

confidence: 99%

The digital laser: on-demand laser modes with the click of a button

Forbes¹,

Ngcobo²,

Burger³

et al. 2014

SPIE Proceedings

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In this paper we will outline our recent advances in all-digital control of light. Importantly, we will outline how to create a so-called "digital laser", where a conventional laser mirror is replaced with a phase-only spatial light modulator. This allows the mirror properties to be dynamically changed by altering only the image sent to the device: on-demand laser modes. We demonstrate a myriad of laser beams that can be created from the same device without any realignment or additional custom optics.

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Adaptive mode optimization of a continuous-wave solid-state laser using an intracavity piezoelectric deformable mirror

Cited by 28 publications

References 5 publications

Deep learning control model for adaptive optics systems

Deep learning control model for adaptive optics systems

Wavefront correction performed by a deformable mirror of arbitrary actuator pattern within a multireflection waveguide

The digital laser: on-demand laser modes with the click of a button

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