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 a new approach to designing controllers for nonlinear
systems using data-driven control. Data-driven control is an important area of research in …

In a paper by Willems et al., it was shown that persistently exciting data can be used to
represent the input-output behavior of a linear system. Based on this fundamental result, we …

The use of persistently exciting data has recently been popularized in the context of data-
driven analysis and control. Such data have been used to assess system-theoretic …

In this article, we propose a new method to obtain feedback controllers of an unknown
dynamical system directly from noisy input/state data. The key ingredient of our design is a …

We address the problem of designing a stabilizing closed-loop control law directly from input
and state measurements collected in an experiment. In the presence of a process …

This paper considers the Linear Quadratic Regulator problem for linear systems with
unknown dynamics, a central problem in data-driven control and reinforcement learning. We …

This paper studies several problems related to quadratic matrix inequalities (QMIs), ie,
inequalities in the Loewner order involving quadratic functions of matrix variables. In …

The linear quadratic regulator (LQR) problem has reemerged as an important theoretical
benchmark for reinforcement learning-based control of complex dynamical systems with …

We study the application of a data‐enabled predictive control (DeePC) algorithm for position
control of real‐world nano‐quadcopters. The DeePC algorithm is a finite‐horizon, optimal …