Locally Power-Optimal Nonlinear Model Predictive Control for Fixed-Wing Airborne Wind Energy


Airborne Wind Energy (AWE) is a new way of harvesting the wind’s power via tethered kite systems which has enormous potential, but poses many challenges in practice. One particularly challenging aspect is the control of the kite, or similarly a tethered fixed-wing vehicle. Tethered flight is a highly nonlinear, constrained, and fast dynamic system, requiring careful control design for optimal power producing results. This paper formulates the AWE problem as a practical, high-level Nonlinear Model Predictive Control scheme, balancing an abstracted control augmented modeling approach with the tight computational constraints on board small fixed-wing systems for real-time, long-horizon predictive control. A power objective is developed which trades off tracking performance of a given nominal path with the expected power generation resulting from the aircraft’s trajectory. An analysis of the performance gains in various wind conditions are elaborated per tuning of the power objective, and the controller is validated in simulation before deployment on a small development platform. The control system is demonstrated in practice in an exemplary tethered flight experiment.

Proceedings of the 2019 American Control Conference (ACC)