Robust Model Predictive Control of Unmanned Aerial Vehicles Using Waysets

Abstract

This paper introduces a new formulation of model predictive control for robust trajectory guidance of unmanned aerial vehicles. It generalizes the ubiquitous concept of waypoints to waysets, in order to provide robustness to bounded state disturbances in the presence of obstacles. Using a variable horizon formulation of model predictive control, it shows how wayset guidance combined with constraint tightening can guarantee robust recursive feasibility and finite-time completion of a control maneuver. Simulations on a point mass fixed-wing unmanned aerial vehicle model moving through a field of obstacles with wind disturbances demonstrate significant computational benefits from using waysets ..