Nonlinear Model Predictive Guidance for Fixed-wing UAVs Using Identified Control Augmented Dynamics.
In this paper, we address the modeling and identification of controlaugmented dynamics for a small fixed-wing Unmanned Aerial Vehicle (UAV) with awidely available off-the-shelf (OTS) autopilot in the loop, utilizing astandard sensor suite. A high-level Nonlinear Model Predictive Controller(NMPC) is subsequently formulated for simultaneous airspeed stabilization, pathfollowing, and soft constraint handling, using the identified model for horizonpropagation. The approach is explored in several exemplary flight experimentsincluding path following of helix and connected Dubins Aircraft segments inhigh winds as well as a motor failure scenario. The cost function, insights onits weighting, and additional soft constraints used throughout theexperimentation are discussed.
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