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).

System theoretic tools in Adaptive Optics

BEGHI, ALESSANDRO;CENEDESE, ANGELO;MASIERO, ANDREA
2009

Abstract

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).
2009
Proceedings of the 2009 IEEE International Conference on Control and Automation
9781424447060
9781424447077
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2445526
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