Flight path optimization and predictive control of an airborne wind energy system

Abstract

The emerging field of Airborne Wind Energy (AWE) uses tethered aircraft to harvest high-altitude wind energy. This master thesis presents the hardware and software architecture of a small-scale AWE system prototype consisting of a foam glider and a ground station. The aerodynamic coefficients of the flying vehicle are identified using a dynamic optimization-based algorithm with the data collected in flight experiments. For the automatic operation of the airplane and ground station, a cascaded architecture for AWE-specific flight and tether tension control are suggested. Furthermore, a nonlinear model predictive flight controller is developed based on the identified model. The algorithms are tested in flight experiments.