Robust longitudinal-lateral look-ahead pursuit path-following control: Fast finite-time stability and optimal...

This paper addresses the robust path-following problem of fixed-wing unmanned aerial vehicles (UAVs) under bounded external disturbances and non-smooth predefined paths. In low-altitude mountainous/urban terrains with wind disturbances, the inherent aerodynamic characteristics and flight constraints (e.g., bank angle limits, load factor) of fixed-wing UAVs severely challenge accurate and rapidly stable path-following. Existing path-following approaches either lack quantitative robustness evaluation, fail to guarantee fast convergence, or are incompatible with non-smooth paths under flight constraints. To resolve these issues, we propose a constraints-aware robust path-following framework with clear connections to existing methods: First, from the perspective of globally disturbed attractors, we innovatively design robustness metrics that quantify both the exponential convergence rate and the bounds of the ultimate attractor set, providing a quantitative basis for robustness optimization. Second, we develop a robust longitudinal-lateral look-ahead pursuit (RLLP) guidance law, explicitly accounting for flight path angle, track angle, and external disturbances while adapting to fixed-wing UAV dynamics. Third, we derive an optimized variant (Optimal-RLLP) to enhance robustness and establish sufficient conditions for fast finite-time stability-ensuring finite-time convergence, strong disturbance resistance, and reduced sensitivity to constrained uncertainties. High-fidelity simulations, including comparative analyses and implementation-related validations, demonstrate the feasibility, optimality, and robustness of the proposed methods under atmospheric disturbances. Besides, the work also offers actionable engineering principles.