This paper presents a framework by which a data-driven optimization technique known as
Bayesian Optimization can be used for real-time optimal control. In particular, Bayesian …

This study presents a data-driven approach for optimizing the operating altitude of Airborne
Wind Energy (AWE) systems to maximize net energy production. Determining the optimal …

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 …

We present a data-driven optimization framework that aims to address online adaptation of
the flight path shape for an airborne wind energy system (AWE) that follows a repetitive path …

This paper presents a novel data-driven nested optimization framework that aims to solve
the problem of coupling between plant and controller optimization. This optimization strategy …

This paper presents a novel data-driven nested optimization framework that addresses the
problem of coupling between plant and controller optimization. This optimization strategy is …

This paper describes a data-driven approach for real-time control of a physical system.
Specifically, this paper focuses on the cooperative wind farm control where the objective is …

This paper discusses a data-driven, cooperative control strategy to maximize wind farm
power production. Conventionally, every wind turbine in a wind farm is operated to maximize …

The objective of this study is to develop a model-free optimization algorithm to improve the
total wind farm power production in a cooperative game framework. Conventionally, for a …

Optimal control in a spatiotemporally varying environment is difficult, especially if the
environment is partially observable. Altitude optimization of an airborne wind energy (AWE) …