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Adaptive optics (AO) systems for ground-based telescopes use deformable mirrors to physically correct wavefront distortions induced by atmospheric turbulence. Due to time delays caused by different parts of the AO system, the process of turbulence correction becomes even more difficult since the earth’s atmosphere changes continuously. In this paper we propose a new temporal control approach for the computation of optimal mirror configurations based on the solution of a sequence of inverse problems for the wavefront sensor operator. Our mathematical formulation of the underlying problem allows the incorporation of computationally efficient wavefront reconstruction methods and a wavefront prediction step. Based on the frozen flow assumption, the prediction of a future wavefront relies on a suitable shift of the reconstructed wavefront. The performance of our temporal control algorithm is demonstrated in the context of a single conjugate adaptive optics system on a 37 meter telescope using a Shack–Hartmann wavefront sensor. Numerical results of the proposed control method are provided using OCTOPUS, the official end-to-end simulation tool of the European Southern Observatory.

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In wide-field applications of adaptive optics systems, the problem of atmospheric tomography has to be solved. Commonly used methods for this purpose operate on a set of two-dimensional reconstruction layers. Due to run-time restrictions and demands on stability, in general the usable number of such reconstruction layers is less than the number of atmospheric turbulence layers. Hence, model reduction has to be applied to the profile of atmosphere layers in order to achieve a smaller number of the most relevant reconstruction layers. In continuation of earlier published and purely heuristic experiments, we concentrate on the question how the choice of the heights of these reconstruction layers influences the performance of the tomographic solver, aiming for a more rigorous analysis. We derive a function representing an approximate expected value for the best-case residual error, i.e., a limitation (in a statistical sense) for what any tomographic solver is able to reach. We provide a method for the minimization of this function, which consequently yields an algorithm for the (approximately) optimal choice of the reconstruction layer heights for a given input atmosphere model, i.e., given the turbulence strength depending on the altitude. Our implementation of the optimization algorithm has acceptable run-time, and first tests of the resulting layer profiles show that the obtained quality is significantly better than for other choices of the reconstruction layer profiles.

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