Learning-enhanced hybrid control for beam jitter suppression in LEO-GEO optical communication links.

This paper proposes a physics-guided hybrid control framework for real-time suppression of micro-vibration-induced beam jitter in long-distance LEO-GEO (low Earth orbit-geostationary Earth orbit) optical communication links. Building on a disturbance-observer-based Linear Quadratic Integral architecture with output-only autoregressive identification, the approach removes the need for explicit disturbance input measurements while preserving modeling accuracy. A lightweight neural module, implemented via a custom-designed Transformer model, is incorporated as a bounded residual compensator to refine control performance under non-stationary disturbances, and is further safeguarded by a gating mechanism to ensure stable fallback operation. Experiments on a production-grade optical terminal under representative multi-condition disturbances show consistently high identification correlation (up to 0.9997) and a substantial reduction in RMS jitter from 0.324918 to 0.04841µrad, with only 0.3% additional computational overhead, outperforming conventional control approaches. These results support reliable and efficient deployment for high-precision beam stabilization in spaceborne optical systems.