Design and Experimental Validation of a Model-Free Controller for Beam Stabilization in Adaptive Optics Systems

Abstract

Stabilization of optical beams has always been a key factor affecting the performance of many optical systems. The Adaptive optics (AO) beam stabilization system requires further development to cope with increasingly complex application scenarios and challenges. Motivated by this, a new filter-based off-policy policy iteration (FB-OPPI) control scheme is proposed and experimentally verified in this paper to provide AO systems with a flexible beam stabilization method. The FB-OPPI is based on the policy iteration, a model-free controller design principle. To address the challenges such as convergence speed, data requirements and control stability that it faces in practice, we have proposed an implicit state reconstruction method based on the Kalman filter and introduced the adaptive transverse filter technology. Additionally, the off-policy learning mechanism is deployed to simplify the optimization process. An AO beam stabilization system was constructed to verify the effectiveness of the proposed method. Experimental results show that the FB-OPPI method features simple design and fast training, releases the requirement for additional sensors or model recognition. The FB-OPPI method is superior to traditional integral controllers, effectively handling high-frequency narrowband and complex beam jitters. Despite not requiring model identification, it is on par with the advanced Linear Quadratic Gaussian (LQG) control.