Model-guided online calibration for multi-actuator active optical systems with strong nonlinearities and inter-actuator coupling

Active piezo-driven optical systems produced by low-precision but low-cost mesoscale fabrication, suffer from strong actuator coupling, hysteresis, creep, and fabrication imperfections that render conventional influence-matrix calibration ineffective. Purely data-driven methods, while expressive, require excessive training data and lack real-time feasibility. We introduce a lightweight closed-loop calibration framework that uses an available nominal physics-based model as an informed prior. Starting from model-predicted commands, closed-loop position feedback iteratively refines the actuator commands. By leverging the nominal model, the correction landscape becomes low-rank and well-conditioned, achieving convergence in <15 iterations with residual pointing errors below 4% RMS of full stroke—an 87% improvement over uncorrected open-loop operation—without explicit modeling of hysteresis and other nonidealities. The rapidly collected calibration data can also train a compact feed-forward neural network for subsequent high-bandwidth open-loop use. This hybrid approach combines model-based stability with data-driven flexibility, significantly relaxes fabrication tolerances, and enables precision using inexpensive manufacturing equipment.