System theoretic tools in Adaptive Optics

Adaptive optics (AO) systems provide a real challenge to the control engineer in many respects, the foremost of which are scalability and computational complexity of the control algorithms. On the other hand, systems theoretic tools can be applied to look at several problems under new perspectives. In this paper, we review a recent stochastic realization based method for turbulence simulation. Then, we investigate the estimation of the turbulence structure (i.e. the characteristics of its layers) through the use of a Markov random field (MRF) representation. Finally, we present a subspace algorithm for the identification of a dynamic model of the turbulence. The proposed method exploits the previously estimated turbulence characteristics to perform the first step of classical subspace identification procedures (Ho-Kalman's algorithm).