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 study the problem of finite-time constrained optimal control of unknown
stochastic linear time-invariant (LTI) systems, which is the key ingredient of a predictive …

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 …

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 …

Over the last decades, model predictive control (MPC) has shown outstanding performance
for control tasks from various domains. This performance has further improved in recent …

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 …

For the control of connected and autonomous vehicles (CAVs), most existing methods focus
on model-based strategies. They require explicit knowledge of car-following dynamics of …

Data-driven control approaches for the minimization of energy consumption of buildings
have the potential to significantly reduce deployment costs and increase the uptake of …