We study the problem of performance optimization of closed-loop control systems with
unmodeled dynamics. Bayesian optimization (BO) has been demonstrated effective for …

Kites can be used to harvest wind energy at higher altitudes while using only a fraction of the
material required for conventional wind turbines. In this work, we present the kite system of …

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 …

We study the problem of performance optimization of closed-loop control systems with
unmodeled dynamics. Bayesian optimization (BO) has been demonstrated to be effective for …

Emukit is a highly flexible Python toolkit for enriching decision making under uncertainty with
statistical emulation. It is particularly pertinent to complex processes and simulations where …

Characterizing evolving ocean environments is important to scientific, renewable energy,
and military applications. However, performing meaningful characterizations of these …

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 …

In this paper, we present an online approach for optimizing the 3D layout of a reconfigurable
ocean current turbine (OCT) array. Unlike towered turbines, most OCT concepts for Gulf …

In this paper, the CONFIG algorithm, a simple and provably efficient constrained global
optimization algorithm, is applied to optimize the closed-loop control performance of an …

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 …