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