Knowledge-driven adaptive alignment method for reflective optical systems based on physics-informed multi-task...

The precision of optical mirror alignment is fundamental to ensuring optical system performance. However, traditional manual alignment is limited by its heavy reliance on expert experience, while existing data-driven approaches are constrained by their demand for massive high-quality datasets. To address these dual challenges, this study proposes a knowledge-driven adaptive alignment framework that integrates a fuzzy logic system with a physically constrained multi-task learning (MTL) neural network, enabling adaptive adjustment across varying mirrors. SHAP dependence analysis of simulation data is conducted to obtain a data-driven directional consistency prior, which is embedded into the MTL loss function as directional constraints. Subsequently, a fuzzy logic system is developed based on expert knowledge and its step-size outputs are dynamically modulated by the step size scaling factor learnt by MTL model. For the source domain mirror, the proposed method reduces the alignment iterations by 42.8% and decreases final peak-to-valley (PV) and root-mean-square (RMS) by 4.96% and 0.5% respectively, compared to a conventional fuzzy controller. Crucially, when applied towhat is preceived to be a new mirror model, the proposed method reduces the iterations by 55.6% and decreases PV and RMS by 6.9% and 13.7% respectively, compared with manual adjustment. This approach provides a robust, cross-model generalizable foundation for highly efficient automated optical assembly.