The development of control methods based on data has seen a surge of interest in recent
years. When applying data-driven controllers in real-world applications, providing theoretical …

Mathematical optimization is one of the cornerstones of modern engineering research and
practice. Yet, throughout all application domains, mathematical optimization is, for the most …

We introduce a general framework for robust data-enabled predictive control (DeePC) for
linear time-invariant systems, which enables us to obtain robust and optimal control in a …

Data-driven predictive control (DPC) has gained an increased interest as an alternative to
model predictive control in recent years, since it requires less system knowledge for …

This article presents a robust and kernelized data-enabled predictive control (RoKDeePC)
algorithm to perform model-free optimal control for nonlinear systems using only input and …

The existing nonparametric frequency response estimation methods suffer from leakage.
Because of this, these methods do not yield the correct result in case of exact data of a linear …

Traditionally, numerical algorithms are seen as isolated pieces of code confined to an in
silico existence. However, this perspective is inappropriate for many modern computational …

Non-parametric representations of dynamical systems based on the image of a Hankel
matrix of data are extensively used for data-driven control. However, if samples of data are …

The increasing integration of variable renewable energy resources through power
electronics has brought about substantial changes in the structure and dynamics of modern …

In recent years, the amount of data available from systems has drastically increased,
motivating the use of direct data-driven control techniques that avoid the need of parametric …