LQG adaptive optics control with wind-dependent turbulent models

Current advanced control strategies developed in Adaptive Optics field are based on optimal techniques in order to reject the effect of atmosphere turbulence on the phase of wavefront of the incoming light. The widely researched LQG controller relies basically on models that try to capture the temporal evolution of the atmospheric wavefront. Models based on both Cn 2 spatial priors and temporal dynamics have been proposed for SCAO and MOAO systems. The attractive performance of LQG is based on having an accurate model of the wavefront distortion. However, abrupt changes in conditions that directly affect the atmosphere produce a significant decrease in the LQG performance. To deal with this problem, an Active Disturbance Rejection Control (ADRC) strategy is proposed. ADRC, is an unconventional design strategy capable of overcoming the internal dynamics of the system and the external disturbances. The key idea is to treat the unknown information mainly due to atmosphere turbulence as state variable that can be estimated by an Extended State Observer (ESO). Instead of depending on a model, the controller draws the information needed to control the system from the ESO. Consequently, the control law for the Adaptive Optics system can be designed without the mathematical expression of the disturbances. As a result, the combined effect of the non-considered internal dynamics and the external disturbances is estimated and actively rejected. This contribution is focused on adapting ADRC method to Adaptive Optics requirements.

show abstract

“…Some works have demonstrated the robustness of this strategy for the control of astronomical adaptive optics 7,12,17 .…”

Section: Lqg Controlmentioning

confidence: 99%

“…It relies on model that tries to capture temporal evolution of the atmospheric wavefront 6 . Models based on both Cn 2 spatial prior and temporal dynamics have been proposed for SCAO and MOAO systems 7 . However, these models have not shown accuracy enough to improve the controller performance.…”

Section: Introductionmentioning

confidence: 99%

ADRC: a strategy to deal with Adaptive Optics feedback control

González-Cava¹,

Ramos²,

Cagigal³

et al. 2017

Proceedings of the Adaptive Optics for Extremely Large Telescopes 5

View full text Add to dashboard Cite

show abstract

“…The developments of more efficient models is here a key point, and several solutions have been proposed in order to take advantage of the frozen flow characteristics of the turbulence 39 through explicit (parameterized) or implicit (non parameterized) wind-direction-dependent models. 2,15,18,23,25,28,38,40 The computational complexity and possible degree of parallelization is then extremely variable.…”

Section: Minimum Variance Ao Control: Performance Trendsmentioning

confidence: 99%

Towards minimum-variance control of ELTs AO systems

Kulcsár¹,

Raynaud²,

Conan³

et al. 2017

Proceedings of the Adaptive Optics for Extremely Large Telescopes 5

Self Cite

View full text Add to dashboard Cite

Minimum-variance control of adaptive optics (AO) systems relies on a stochastic dynamical model of the perturbation and on models of the components, including loop delays. Resulting LQG controllers have been implemented in SCAO and WFAO both on laboratory benches and on-sky. Their efficiency has been recognized in several modes of operation, e.g. i) on-sky control of TT or low-order modes with vibration mitigation (SPHERE, GPI, CANARY, Raven, GeMS, in H2 formulation at the McMath-Pierce solar telescope) ii) full SCAO mode (CANARY) and MOAO mode (CANARY, Raven) and iv) in general it is advocated to control the low-order modes in laser tomography systems (E-ELT HARMONI LTAO, NFIRAOS). We first point out two examples related to VLT AO controllers to illustrate the need for RTC flexibility. The implementation of LQG control in the framework of the future ELTs raises many questions related both to real-time control computation and associated parameter updates (at a far lower rate), and to the performance that can be reached compared with simpler control strategies. By gathering many lab and on-sky results, we draw the performance trends observed so far. We then outline some promising research directions for control design and implementations for future ELTs AO systems.

show abstract

“…Atmospheric characterization for a ground-based telescope has become a key step in the design of instrumentation to correct for wave-front aberrations introduced by atmospheric turbulence. Adaptive Optics (AO) compensates the wave-front aberrations in real-time and the benefits gained from knowledge of the atmospheric profile, or C 2 n (h), include operating the tomographic turbulence compensation in wide-field using multiple Guide Stars (GS) (Ono et al 2018(Ono et al , 2016Correia et al 2015;Vidal et al 2010), enabling phase predictive control Males & Guyon 2018;Juvénal et al 2016;Sivo et al 2015;Rudy et al 2014;Petit et al 2014) for optimum AO performance, or providing a comprehensive analysis of AO residuals (Ferreira et al 2018;Martin et al 2017). High altitude layers play a major role in the spatial phase decorrelation (Roddier 1981) and has an impact on wide-field AO performance (Costille & Fusco 2012) and extreme AO (Cantalloube et al 2018).…”

Section: Introductionmentioning

confidence: 99%

PEPITO: atmospheric Profiling from short-Exposure focal Plane Images in seeing-limiTed mOde

Beltramo-Martin

Bharmal

Correia

2019

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

Atmospheric profiling is a requirement for controlling wide-field Adaptive Optics (AO) instruments, analyzing the AO performance with respect to the observing conditions and predicting the Point Spread Function (PSF) spatial variations. We present PEPITO, a new concept for profiling atmospheric turbulence from post facto tip-tilt (TT) corrected short-exposure images. PEPITO utilizes the anisokinetism effect in the images between several stars separated from a reference star, and then produces the profile estimation using a model-fitting methodology, by fitting to the long exposure TT-corrected PSF. PEPITO has a high sensitivity to both C 2 n (h) and L 0 (h) by relying on the full telescope aperture and a large field of view. It then obtains a high vertical resolution (1 m-400 m) configurable by the camera pixel scale, taking advantage of fast statistical convergence (of order of tens of seconds). With only a short exposure-capable large format detector and a numerical complexity independent of the telescope diameter, PEPITO perfectly suits accurate profiling for night optical turbulence site characterization or adaptive optics instruments operations. We demonstrate, in simulation, that the C 2 n (h) and L 0 (h) can be estimated to better than 1% accuracy, from fitted PSFs of magnitude V=11 on a D=0.5 m telescope with a 10 arcmin field of view.

show abstract

LQG adaptive optics control with wind-dependent turbulent models

Cited by 10 publications

References 17 publications

ADRC: a strategy to deal with Adaptive Optics feedback control

ADRC: a strategy to deal with Adaptive Optics feedback control

Towards minimum-variance control of ELTs AO systems

PEPITO: atmospheric Profiling from short-Exposure focal Plane Images in seeing-limiTed mOde

Contact Info

Product

Resources

About