Adaptive Optimal Path Following Guidance for Fixed-Wing Aerial Vehicles

A vast body of research exists on the guidance of autonomous unmanned aerial vehicles, with most approaches relying on geometric relationships and constant gains. While these methods can be optimized for predefined flight paths, they become suboptimal in dynamic scenarios requiring real-time guidance without prior knowledge, sharp turns, or significant variations in path length. This work introduces an optimal guidance algorithm with adaptive gains and inherent robustness to external disturbances. By defining the state weighting matrix as a function of cross-track errors, the proposed approach dynamically adjusts gains to minimize deviations. Additionally, incorporating an integral term into the state-space dynamic model ensures zero steady-state error. Lyapunov stability of the algorithm is demonstrated for all possible state weighting matrices. The algorithm is evaluated in a six-degree-of-freedom simulation environment and validated through real-world flight tests under high-wind conditions. Results demonstrate superior robustness and path-tracking performance compared to widely used proportional navigation methods, particularly in adverse environments.