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The presence of atmospheric turbulence degrades the image quality of ground-based telescopes. Adaptive Optics (AO) is a powerful method to partially correct the wavefront distortion caused by atmospheric turbulence in real-time. MAVIS is a visible band, general purpose MCAO instrument with ambitious goals and a tight error budget, motivating the research of an optimised control scheme. In this talk, we present the development of the optimised Learn and Apply (L&A) method - a model-driven, predictive MCAO supervisory solution. We derive a generalised model that combines low-order and high-order measurements from different wavefront sensors to enable a global reconstruction. Based on that, we develop a second order stochastic algorithm for the pseudo-real-time identification of atmospheric turbulence profile (Learn), and an integrated controller that simplifies the MCAO control into a single loop (Apply). Finally, we implement the optimised L&A into a closed supervisory loop with realistic error sources taken into consideration. As shown by the numerical simulation results, our control scheme brings substantial improvement to the system performance, and only introduces marginal increase in real-time computational load. |
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