Reinforcement Learning Application in Wavefront Sensorless Adaptive Optics System - Zou, Runnan (Université d'Ottawa / University of Ottawa, 2024)
With the increasing exploration of space and widespread use of communication tools worldwide, near-ground satellite communication has emerged as a promising tool in various fields such as aerospace, military, and microscopy. However, the presence of air and water in the atmosphere causes distortion in the light signal, and thus, it is essential for the ground base to retrieve the original signal from the distorted light signal sent from the satellite. Traditionally, Shack-Hartmann sensors or charge-coupled devices are integrated in the system for distortion measurement. In our pursuit of a cost-effective system establishment with optimal performance and enhanced response speed, sensors and charge-coupled devices have been replaced by a photodiode and a single mode fiber in this project. Since the system has limited observation capability, it requires a powerful controller for optimal performance. To address this issue, we have implemented an off-policy reinforcement learning framework, the soft actor-critic, in the adaptive optics system controller. This integration results in a model-free online controller capable of mitigating wavefront distortion. The soft actor-critic controller processes the acquired data matrix from the photodiode and generates a two-dimensional array control signal for the deformable mirror, which corrects the wavefront distortion induced by the atmosphere, and refocusing the signal to maximize the incoming power. The parameters of the soft actor-critic controller have been tuned to achieve optimal system performance. Simulations have been conducted to compare the performance of the proposed controller with respect to wavefront sensor-based methods. The training and verification of the proposed controller have been conducted in both static and semi-dynamic atmospheres, under different atmospheric conditions. Simulation results demonstrate that, in severe atmospheric conditions, the adaptive optics system with the soft actor-critic controller achieves more than 55% and 30% Strehl ratio on average in static and semi-dynamic atmospheres, respectively. Furthermore, the distorted wavefront's power can be concentrated at the center of the focal plane and the fiber, providing an improved signal.
Keywords: wavefront sensorless adaptive optics; reinforcement learning