Haffert et al., 2021 - Google Patents
Data-driven subspace predictive control of adaptive optics for high-contrast imagingHaffert et al., 2021
View PDF- Document ID
- 15418520045529158407
- Author
- Haffert S
- Males J
- Close L
- Van Gorkom K
- Long J
- Hedglen A
- Guyon O
- Schatz L
- Kautz M
- Lumbres J
- Rodack A
- Knight J
- Sun H
- Fogarty K
- Publication year
- Publication venue
- Journal of Astronomical Telescopes, Instruments, and Systems
External Links
Snippet
The search for exoplanets is pushing adaptive optics (AO) systems on ground-based telescopes to their limits. One of the major limitations at small angular separations, exactly where exoplanets are predicted to be, is the servo-lag of the AO systems. The servo-lag …
- 230000003044 adaptive 0 title abstract description 13
Classifications
-
- G—PHYSICS
- G02—OPTICS
- G02B—OPTICAL ELEMENTS, SYSTEMS, OR APPARATUS
- G02B27/00—Other optical systems; Other optical apparatus
- G02B27/42—Diffraction optics, i.e. systems including a diffractive element being designed for providing a diffractive effect
- G02B27/4205—Diffraction optics, i.e. systems including a diffractive element being designed for providing a diffractive effect having a diffractive optical element [DOE] contributing to image formation, e.g. whereby modulation transfer function MTF or optical aberrations are relevant
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G02—OPTICS
- G02B—OPTICAL ELEMENTS, SYSTEMS, OR APPARATUS
- G02B27/00—Other optical systems; Other optical apparatus
- G02B27/0025—Other optical systems; Other optical apparatus for optical correction, e.g. distorsion, aberration
- G02B27/0068—Other optical systems; Other optical apparatus for optical correction, e.g. distorsion, aberration having means for controlling the degree of correction, e.g. using phase modulators, movable elements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Haffert et al. | Data-driven subspace predictive control of adaptive optics for high-contrast imaging | |
Males et al. | Ground-based adaptive optics coronagraphic performance under closed-loop predictive control | |
Le Roux et al. | Optimal control law for classical and multiconjugate adaptive optics | |
Nousiainen et al. | Adaptive optics control using model-based reinforcement learning | |
Landman et al. | Self-optimizing adaptive optics control with reinforcement learning for high-contrast imaging | |
Jolissaint et al. | Analytical modeling of adaptive optics: foundations of the phase spatial power spectrum approach | |
Petit et al. | Linear quadratic Gaussian control for adaptive optics and multiconjugate adaptive optics: experimental and numerical analysis | |
Sun et al. | Identification and adaptive control of a high-contrast focal plane wavefront correction system | |
Wong et al. | Predictive control for adaptive optics using neural networks | |
van Kooten et al. | Impact of time-variant turbulence behavior on prediction for adaptive optics systems | |
Landman et al. | Self-optimizing adaptive optics control with reinforcement learning | |
Jensen-Clem et al. | Demonstrating predictive wavefront control with the Keck II near-infrared pyramid wavefront sensor | |
van Kooten et al. | Predictive wavefront control on Keck II adaptive optics bench: on-sky coronagraphic results | |
Wagner et al. | Point spread function reconstruction for single-conjugate adaptive optics on extremely large telescopes | |
Mocci et al. | PI-shaped LQG control design for adaptive optics systems | |
Ragland et al. | Point spread function determination for Keck adaptive optics | |
Rodack et al. | Millisecond exoplanet imaging: I. method and simulation results | |
Fowler et al. | Tempestas ex machina: a review of machine learning methods for wavefront control | |
Nousiainen et al. | Advances in model-based reinforcement learning for adaptive optics control | |
Johnson et al. | Bulk wind estimation and prediction for adaptive optics control systems | |
Nousiainen et al. | Laboratory experiments of model-based reinforcement learning for adaptive optics control | |
Archinuk et al. | Mitigating the nonlinearities in a pyramid wavefront sensor | |
Landman et al. | Making the unmodulated Pyramid wavefront sensor smart-Closed-loop demonstration of neural network wavefront reconstruction with MagAO-X | |
van Kooten et al. | Performance of AO predictive control in the presence of non-stationary turbulence | |
Riggs et al. | Wavefront correction with Kalman filtering for the WFIRST-AFTA coronagraph instrument |