Spatiotemporal Optimization Through Gaussian Process-Based Model Predictive Control: A Case Study in Airborne Wind Energy

Abstract

This brief presents a model predictive control (MPC)-based spatiotemporal optimization strategy that is applied to the problem of optimizing the altitude of a type of airborne wind energy (AWE) system, specifically a buoyant airborne turbine. Altitude optimization for AWE systems represents a challenging problem under which the wind speed varies with both time and altitude, is only instantaneously observable at the altitude where the AWE system is operating, and dictates the net power produced by the system. The proposed MPC strategy avoids the need for a computationally expensive Markov process model for characterizing the wind speed and is structured in a way that the need for instantaneous power maximization (termed exploitation ) is balanced with the need to maintain an accurate map of wind speed versus altitude (termed exploration ). The MPC strategy is calibrated through a Gaussian process regression framework. Real wind speed versus altitude data have been used to validate the strategy.