Optimization of adaptive optics correction during observations: algorithms and system parameters identification in closed-loop

The adaptive optics (AO) on the European Extremely Large Telescope, as well as earlier pathfinders like the Adaptive Optics Facility, at the Very Large Telescope in 2014, will no longer be stationary systems. AO is no longer isolated on a bench; some elements are directly in the optical train of the telescope, suffering environment and constrains changes during the observations. To guarantee good performance at any observing time, we investigate a self-calibration strategy. We focus here on one of the most challenging aspects: the identification of system parameters during closed-loop observations without introducing any additional disturbance. Such problem is known in the identification theory to be difficult to solve. We have recently presented (Béchet et al., AO4ELT2 Conference, 2011) an identification method for this, with promising results obtained in simulations. To consolidate these advances, we come back in the present paper to the equations and provide a theoretical analysis to justify the choice of the algorithm. We highlight the benefit of using incremental data and commands to decorrelate the disturbance. We also present 2 implementations of the method, currently studied at the European Southern Observatory.