Physics-informed machine learning for precision Unmanned Aerial Vehicle control: Adaptive transformers with safety guarantees

Industrial Unmanned Aerial Vehicle (UAV) applications requiring centimeter-level precision face fundamental limitations when traditional control methods encounter complex aerodynamic environments and safety constraints. This paper makes three contributions: (1) a multi-component architecture achieving 38.6 percentage point improvement over standard reinforcement learning for UAV precision control; (2) adaptive three-phase safety constraint optimization with formal convergence guarantees enabling 11.6 percentage point precision gain; (3) analysis of component interactions showing that decision transformers drive performance gains while physics constraints enforce realism. The framework integrates Physics-Informed Neural Networks (PINNs), Neural Operators, Decision Transformers, and Control Barrier Functions (CBFs). Through ablation studies across eight configurations, we evaluate how different physics-informed components interact. The framework achieves 91.9% precision within 10 cm tolerance, 55.3% within 5 cm, and 5.5% within 2 cm while maintaining 99.9% physics consistency. The complete system requires 5.6 h of training on consumer hardware, making practical deployment feasible. Our analysis shows that carefully calibrated safety-precision optimization, rather than rigid constraint enforcement, produces better performance while maintaining safety guarantees in precision-critical autonomous systems.