A Cascaded MPC Framework for Airborne Wind Energy Systems

Airborne Wind Energy is an innovative approach to generate electricity from high-altitude winds. Despite advances in AWES control, controlling the aircraft in the face of turbulence, unsteady aerodynamics, and rapid wind variations remains a challenge. Model Predictive Control (MPC) shows promising results, but relies on precise models. To improve MPC performance in the presence of uncertainty, a cascaded control strategy is proposed, integrating MPC at its first stage. This study investigates the effectiveness of Incremental Nonlinear Dynamic Inversion (INDI) and L1 adaptive control for the second stage, assessing their ability to manage rotational dynamics uncertainties and mitigate disturbances. The controller performance is validated in a realistic simulation that incorporates Gaussian Process (GP) models trained on Computational Fluid Dynamics (CFD) data.