UAV energy extraction with incomplete atmospheric data using MPC

A nonlinear model predictive control strategy is presented for unmanned aerial vehicle (UAV) trajectory determination. The objective is to find optimal paths in the atmosphere by maximizing the UAV's energy (kinetic and potential) over a finite but receding horizon. The main assumption is that the updraft distribution is unknown, creating a realistic situation. The updrafts are only estimated online using standard on-board inertial sensors. Real-time implementation of the algorithm is shown to be possible in principle.