Closed-Loop Predictive Control for Adaptive Optics via Neural Networks

Wavefront sensor-less adaptive optics (WFS-less AO) systems have garnered considerable interest in recent years due to their compact architecture and extensive applicability. However, most algorithms developed for adaptive optics systems predominately rely on conventional control methodologies, which often take an unexpectedly prolonged period to converge and severely hinder the feasibility of practical applications, especially in scenarios where rapid environmental fluctuations necessitate real-time control capabilities. Therefore, we propose an efficient closed-loop wavefront reconstruction method based on a novel forward prediction paradigm. The method leverages information from the current control objective, the wavefront phase, and the previous control signals (typically the voltages applied to deformable mirrors) to predict wavefront distortions and implement anticipatory control actions accordingly. Experimental results demonstrate that, compared to most conventional techniques, our method can reduce the post-control RMS wavefront aberration by nearly one-third while exhibiting robustness to different types of turbulence and showing promising potential for complex multi-layer turbulence scenarios.