Wind estimation and prediction for adaptive optics control systems

Performance of adaptive optics (AO) systems is limited by the tradeoff between photon noise at the wavefront sensor and temporal error from the duty cycle of the controller. Optimal control studies have shown that this temporal error can be reduced by predicting the turbulence evolution during the control cycle. We formulate a wind model that divides the wind into two components: a quasi-static layer and a wind-driven frozen-flow layer. Using this internal wind model, we design a computationally efficient controller that is able to estimate and predict the dynamics of a single windblown layer and Simulate this controller using on-sky data from the Palomar Adaptive Optics system. We also present results from a laboratory implementation of multi-conjugate AO (MCAO) with multi-layer wind estimation in conjunction with tomographic reconstruction. The tomography engine breaks the atmosphere into discrete layers, each with its own wind estimator. The resulting MCAO control algorithm is able to track and predict the motion of multiple wind layers with wind estimates that update at every controller cycle. Once the wind velocities of each layer are known, the deformable mirror update speed is no longer limited by the wavefront sensor exposure time so it is possible to send multiple correction updates to the deformable mirror each control cycle in order to dynamically track wind layers across the telescope aperture. The result is better dynamics in the feedback control system that enables higher closed-loop bandwidth for a given wavefront sensor frame rate.