Automatic disturbance identification for linear quadratic Gaussian control in adaptive optics

Abstract

ABSTRACT Linear quadratic Gaussian (LQG) control is an appealing control strategy to mitigate disturbances in adaptive optics (AO) systems. The key of this method is to quickly and consecutively build an accurate dynamical model to track time-varying disturbances such as turbulence, wind load and vibrations. In order to address this problem, we propose an automatic identification method consisting mainly of an improved spectrum separation procedure and a parameter optimization process based on the particle swarm optimization (PSO) algorithm. The improved spectrum separation can pick out perturbation peaks more accurately, especially when some peaks are very close together. Moreover, compared with the Levenberg–Marquardt method and the maximum-likelihood technique based on grids, the PSO algorithm has a faster convergence speed and lower computational burden, and thus is easier to implement. The entire identification process can run automatically online without human intervention. This identification method is verified with a synthetic disturbance profile in a simulation. Furthermore, the performance of the method is evaluated with consecutive measurement data recorded by the 1-m New Vacuum Solar Telescope at the Fuxian Solar Observatory.