Data-Driven Optimal Control for Adaptive Optics

TLDR: In this article, a data-driven optimal control strategy for adaptive optics (AO) systems is proposed, which consists of a dedicated subspace-identification algorithm to identify an atmospheric disturbance model from open-loop sensor data, followed by an optimal control design.

Abstract: Adaptive optics (AO) is a technique to actively correct the wavefront distortions introduced in a light beam as it propagates through a turbulent medium. Nowadays, it is commonly applied in ground-based telescopes to counteract the devastating effect of atmospheric turbulence. This thesis focuses on the control aspects of AO. Whereas most AO systems use a simple control law derived from physical insights, this thesis addresses the AO control problem from a control engineering perspective. The main objective is to show that the performance of the current generation of AO systems can be improved by applying advanced control strategies that account for the AO system dynamics and the spatio-temporal correlation in the wavefront. To this end a data-driven optimal control is proposed. It consists of a dedicated subspace-identification algorithm to identify an atmospheric disturbance model from open-loop wavefront sensor data, followed by an optimal control design. By an efficient implementation, this approach can be used to design a full multi-variable controller for AO systems with up to a few hundred sensors and actuators, without assuming any form of decoupling. The data-driven optimal control strategy is thoroughly tested both in simulations and on an AO laboratory setup. The experiments show, that compared to the standard approach, optimal control is able to improve the wavefront suppression performance, especially under low light level conditions and rough turbulence. The gain in performance is explained the improved ability to anticipate future distortions when accounting for the spatio-temporal correlation in the wavefront.