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 …

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 …

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 …

In this article, we present a novel data-driven model predictive control (MPC) approach to
control unknown nonlinear systems using only measured input–output data with closed-loop …

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 …

Optimization-based control strategies are an affirmed research topic in the area of electric
motor drives. These methods typically rely on the accurate parametric representation of …

Autonomous marine vehicles (AMVs) have received considerable attention in the past few
decades, mainly because they play essential roles in broad marine applications such as …

This paper provides an overview of the recent research efforts on the integration of machine
learning and model predictive control under uncertainty. The paper is organized as a …

We present a framework for systematically combining data of an unknown linear time-
invariant system with prior knowledge on the system matrices or on the uncertainty for robust …

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 …