The behavioral approach to systems theory, put forward 40 years ago by Jan C. Willems,
takes a representation-free perspective of a dynamical system as a set of trajectories. Till …

This article provides an overview of certain direct data-driven control results, where control
sequences are computed from (noisy) data collected during offline control experiments …

This letter develops a method to learn optimal controls from data for bilinear systems without
a priori knowledge of the dynamics. Given an unknown bilinear system, we characterize …

We introduce a novel data-driven model-reference control design approach for unknown
linear systems with fully measurable state. The proposed control action is composed by a …

Controllability Gramians are the most widely used measures of controllability, which quantify
the reachable state sets of systems with a control input of unit energy. They are utilized as a …

When dealing with unknown systems, data can be used to directly learn controllers with
desirable features, thus bypassing system identification. In this paper we present strategies …

We show how the recent works on data-driven open-loop minimum-energy control for linear
systems can be exploited to obtain closed-loop control laws in the form of linear dynamic …

While classic controller design methods rely on a model of the underlying dynamics, data-
driven methods allow to compute controllers leveraging solely a set of previously recorded …

We show how the recent works on data driven open-loop minimum-energy control for linear
systems can be exploited to obtain closed-loop piecewise-affine control laws, by employing …

We show how it is possible to synthesize a stabilizing feedback control, in the complete
absence of a model, starting from the open-loop control generated by an expert operator …