Integrated Modeling of Telescopes

Andersen, Torben; Enmark, Anita

doi:10.1007/978-1-4614-0149-0

Cited by 21 publications

(15 citation statements)

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“…The A matrix is basically a normalized sensitivity matrix, or a Jacobian matrix consisting of the first order partial derivatives of the wave maps for a set of desired perturbations [6]. The mapping of the actuator length adjustment into the rigid-body-motions is not modeled in the present study and it is assumed all the degrees of freedom are achievable by combined motions of six actuators attached to each mirror to a high degree of fidelity.…”

Section: Construction and Characteristics Of Influence Matrixmentioning

confidence: 99%

Alignment estimation and control of the James Webb Space Telescope mirrors using decomposition of an influence matrix

Shiri

Howard

Aronstein

2011

Integrated Modeling of Complex Optomechanical Systems

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The James Webb Space Telescope (JWST) is a three-mirror anastigmatic telescope. For a "blind" telescope alignment estimation and control study at the Goddard Space Flight Center (GSFC), influence functions for each of the primary mirror's 18 segments (6 degrees of freedom each) and for the secondary mirror (5 degrees of freedom) were used to construct a 113-element influence matrix. The singular value vector obtained from pseudoinverse of this influence matrix indicates that the most dominant modes in the control of the telescope are rotation, piston, clocking and translation, in order of significance. The application of an iterative-control algorithm to ten cases of random misalignments, involving both the primary segmented mirror and secondary mirror, shows strong convergence after 5 iterations in most of the cases.

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Section: Construction and Characteristics Of Influence Matrixmentioning

confidence: 99%

Alignment estimation and control of the James Webb Space Telescope mirrors using decomposition of an influence matrix

Shiri

Howard

Aronstein

2011

Integrated Modeling of Complex Optomechanical Systems

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“…To find these, we followed established procedures. 15 For each data set, we first determined a phase screen at the entrance pupil of the telescope. The atmosphere was modeled with the "seven-layer model" with Fried's parameter and coherence time values of E Q -T A R G E T ; t e m p : i n t r a l i n k -; s e c 2 .…”

Section: Training and Validation Datamentioning

confidence: 99%

“…For the case in which PSF time correlation was needed for the neural network study, we generated the phase screens using the slower "Skylight" method, also described in Ref. 15. For each of the layers, the phase screen was simply computed as a sum of two-dimensional harmonic functions with random directions and phases: E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 3 ; 1 1 6 ; 5 2 8 φðx; yÞ ¼…”

Section: Training and Validation Datamentioning

confidence: 99%

“…The values for coefficients Aðf j Þ depend on r 0 and are given in Ref. 15. We used a Taylor approximation and computed "frozen" phase screens for all seven atmospheric layers, and before adding all seven phase screens, we moved them with xand y-velocities given by the following two vectors for x and y directions: We compared phase screens found using the power spectrum and the Skylight methods by numerically computing the phase structure function.…”

Section: Training and Validation Datamentioning

confidence: 99%

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Image-based wavefront sensing for astronomy using neural networks

Andersen

Owner-Petersen

Enmark

2020

J. Astron. Telesc. Instrum. Syst.

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Motivated by the potential of nondiffraction limited, real-time computational image sharpening with neural networks in astronomical telescopes, we studied wavefront sensing with convolutional neural networks based on a pair of in-focus and out-of-focus point spread functions. By simulation, we generated a large dataset for training and validation of neural networks and trained several networks to estimate Zernike polynomial approximations for the incoming wavefront. We included the effect of noise, guide star magnitude, blurring by wide-band imaging, and bit depth. We conclude that the "ResNet" works well for our purpose, with a wavefront RMS error of 130 nm for r 0 ¼ 0.3 m, guide star magnitudes 4 to 8, and inference time of 8 ms. It can also be applied for closed-loop operation in an adaptive optics system. We also studied the possible use of a Kalman filter or a recurrent neural network and found that they were not beneficial to the performance of our wavefront sensor. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…The dynamic behavior of the faceplate is described by where M, E, K are respectively the mass, damping and stiffness matrices, f is a force vector and ξ is a vector of angular and translational displacements. Guyan reduction and modal truncation [11] were used to obtain a more computationally practical model. Guyan reduction reduced the number degrees of freedom for each node to three, retaining out-of plane translation and the two rotations around the in-plane axes.…”

Section: Introduction and Conceptmentioning

confidence: 99%

Distributed control of large deformable mirrors

et al. 2012

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While it is attractive to integrate a deformable mirror (DM) for adaptive optics (AO) into the telescope itself rather than using relay optics within an instrument, the resulting large DM can be expensive, particularly for extremely large telescopes. A low-cost approach for building a large DM is to use voice-coil actuators, and rely on feedback from mechanical sensors to improve the dynamic response of the mirror sufficiently so that it can be used in a standard AO control system. The use of inexpensive voice-coil actuators results in many lightlydamped structural resonances within the desired control bandwidth. We present a robust control approach for this problem, and demonstrate performance in a closed-loop AO simulation, incorporating realistic models of low-cost actuators and sensors. The first contribution is to demonstrate that high-bandwidth active damping can be robustly implemented even with non-collocated sensors, by relying on the "acoustic limit" of the structure where the modal bandwidth exceeds the modal spacing. Next we introduce a novel local control approach, which significantly improves the high spatial frequency performance relative to collocated position control, but without the robustness challenges associated with a global control approach. The combination of these "inner" control loops results in DM command response that is demonstrated to be sufficient for integration within an AO system.

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Integrated Modeling of Telescopes

Cited by 21 publications

References 0 publications

Alignment estimation and control of the James Webb Space Telescope mirrors using decomposition of an influence matrix

Alignment estimation and control of the James Webb Space Telescope mirrors using decomposition of an influence matrix

Image-based wavefront sensing for astronomy using neural networks

Distributed control of large deformable mirrors

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