Model predictive control-based adaptive optics system with deep-learning Shack-Hartmann wavefront sensor

Model predictive control (MPC) can use the state of the current measurement processing to predict future events and be able to take control processing accordingly. To implement MPC in our adaptive optics system (AOS), a multichannel state-space model is first identified between the driving voltage for a 61-channel deformable mirror (DM) as the input and the 8-order Zernike polynomial coefficients via a lab-made Shack-Hartmann wavefront sensor (SHWS) as the output. Conventionally, the center of weight algorithm is utilized to reconstruct the wavefront from SHWS, but it takes a lot of computation time. Therefore, a deep learning (DL) approach based on U-Net is adopted to rapid reconstruct the wavefront. The U-Net significantly reduces the time to compute the wavefront and also gets the higher accuracy. After that, the MPC controller based on the identified system model is implemented in AOS. Currently, the simulation results demonstrate that the MPC with the DL-SHWS can fast correct the wavefront aberration. Eventually, the MPC-based AOS will be implemented under Robot Operating System (ROS) to achieve real-time control.