Optimization-Based Predictive Iterative Learning Control for Deformable Mirrors

As a wavefront corrector, the deformable mirror serves as a core component of adaptive optical systems. Its performance directly affects the bandwidth of the overall system. Nevertheless, the control precision is limited by the coupling crosstalk between actuators and the nonlinear characteristics of deformable mirrors. To address this, an optimization-based predictive iterative learning control method is proposed. This method enhances the performance of the deformable mirror, while reducing control output variations, preventing saturation, and minimizing stress-induced mirror damage. Consequently, the lifespan of DMs is expected to be extended. Simulation studies are performed to evaluate both the static and dynamic performance of the proposed method. And it achieves a 48% reduction in root mean square error when compared to the least mean square method in a closed-loop system with a more reasonable voltage distribution of the actuators under static wavefront.