Can, Erol

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in complex, safety–critical missions where robust control under environmental uncertainties—such as in-flight icing and sensor noise—is essential. These disturbances degrade aerodynamic performance, flight stability, and tracking accuracy, challenging conventional control methods. This paper presents a novel control framework that synergistically integrates a fuzzy logic controller (FLC) with online adaptive tuning via Particle Swarm Optimization (PSO), embedded within a port-Hamiltonian (PH) modeling structure. The PH framework ensures physically consistent, energy-aware representation of UAV dynamics, enabling rigorous Lyapunov-based stability analysis and passivity guarantees. The key innovation lies in combining adaptive fuzzy control with an energy-based PH system model, which together enhance robustness against complex, nonlinear disturbances, such as icing and sensor noise, while simultaneously optimizing energy efficiency. Extensive simulations under realistic environmental and parametric uncertainties demonstrate significant improvements over traditional PID and standalone fuzzy controllers, including up to 85.7% reduction in altitude tracking error and 31.6% decrease in total energy consumption. Importantly, the approach maintains computational tractability by employing a simplified planar 3-DOF model focusing on translational and pitch dynamics—acknowledged as a main limitation and direction for future work. This integrated methodology advances the state-of-the-art by bridging adaptive intelligent control with rigorous physics-based modeling, providing a scalable and robust solution for next-generation autonomous UAVs operating safely in uncertain and adverse conditions.