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 this article, we discuss connections between sequential system identification and control
for linear time-invariant systems, often termed indirect data-driven control, as well as a …

The study of resilient control of linear time-invariant (LTI) systems against denial-of-service
(DoS) attacks is gaining popularity in emerging cyber-physical applications. In previous …

Behavioral systems theory decouples the behavior of a system from its representation. A key
result is that, under a persistency of excitation condition, the image of a Hankel matrix …

Data-driven predictive control (DDPC) has been recently proposed as an effective
alternative to traditional model-predictive control (MPC) for its unique features of being time …

We present a model predictive control (MPC) scheme to control linear time-invariant systems
using only measured input-output data and no model knowledge. The scheme includes a …

Roughly speaking, systems and control theory deals with the problem of making a concrete
physical system behave according to certain desired specifications. To achieve this desired …

A recurring question that all authors of this special issue encounter is,“Why not go with
models?” Two terms need to be clarified: In this context, a model is understood as a …

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 …