The Learn and Apply tomographic reconstructor coupled with the pseudo open-loop control scheme shows promising results in simulation for multi-conjugate adaptive optics systems. We motivate, derive, and demonstrate the inclusion of a predictive step in the Learn and Apply tomographic reconstructor based on frozen-flow turbulence assumption. The addition of this predictive step provides an additional gain in performance, especially at larger wave-front sensor exposure periods, with no increase of online computational burden. We provide results using end-to-end numerical simulations for a multi-conjugate adaptive optics system for an 8m telescope based on the MAVIS system design.
The Learn and Apply reconstruction scheme uses the knowledge of atmospheric turbulence to generate a tomographic reconstructor, and its performance is enhanced by the real-time identification of the atmosphere and the wind profile. In this paper we propose a turbulence profiling method that is driven by the atmospheric model. The vertical intensity distribution of turbulence, wind speed and wind direction can be simultaneously estimated from the Laser Guide Star measurements. We introduce the implementation of such a method on a GPU accelerated non-linear least-squares solver, which significantly increases the computation efficiency. Finally, we present simulation results to demonstrate the convergence quality from numerically generated telemetry, the end-to-end Adaptive Optics simulation results, and a time-to-solution analysis, all based on the MAVIS system design.
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