Robust Longitudinal-lateral Look-ahead Pursuit Path-Following Control: Fast Finite-Time Stability Guarantee

TL;DR

This paper introduces a robust, finite-time stable path-following guidance law for fixed-wing UAVs that effectively handles external disturbances and complex, non-smooth paths, ensuring fast and reliable navigation.

Contribution

It proposes a novel robustness metric and a corresponding guidance law with an optimized version, enhancing stability and robustness for UAV path-following under flight constraints and disturbances.

Findings

The guidance law achieves finite-time stability in simulations.

It maintains robustness under wind disturbances.

The optimized guidance law outperforms existing methods.

Abstract

This paper addresses the challenging problem of robust path-following for fixed-wing unmanned aerial vehicles (UAVs) in complex environments with bounded external disturbances and non-smooth predefined paths. Due to the unique aerodynamic characteristics and flight constraints of fixed-wing UAVs, achieving accurate and fast stable path following remains difficult, especially in low-altitude mountainous terrains, urban landscapes, and under wind disturbances. Most existing path-following guidance laws often struggle to ensure fast stabilization under unknown bounded disturbances while maintaining sufficient robustness, and there is a lack of research on optimizing robustness for non-smooth paths under flight constraints. This paper addresses these issues by proposing a constraints-based robust path-following controller. Firstly, from the perspective of global random attractor, we innovatively introduce robustness metrics that quantify both the exponential convergence rate and the range of the ultimate attractor set. Secondly, building on these metrics, we develop a robust longitudinal-lateral look-ahead pursuit (RLLP) guidance law for fixed-wing UAVs, specifically considering the flight path angle and track angle under external disturbances. Thirdly, we also derive an optimized version (Optimal-RLLP) to enhance the robustness metrics, and elaborate on the sufficient conditions for fast finite-time stability, ensuring the guidance law achieves finite-time stability and robustness with reduced sensitivity to constrained uncertainties. The simulation results validate the proposed guidance law's feasibility, optimality and robustness under atmospheric disturbances using a high-fidelity simulation platform and provide key principle for practical deployment.