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 fundamental lemma by Jan C. Willems and co-workers is deeply rooted in behavioral
systems theory and it has become one of the supporting pillars of the recent progress on …

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 …

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 …

We employ a novel data-enabled predictive control (DeePC) algorithm in voltage source
converter (VSC)-based high-voltage DC (HVDC) stations to perform safe and optimal wide …

This article presents a robust and kernelized data-enabled predictive control (RoKDeePC)
algorithm to perform model-free optimal control for nonlinear systems using only input and …

Fuel cell systems constitute an electrochemical energy conversion system increasingly used
in stationary and mobile applications. Complying with operational limits in transient …

This paper contrasts recursive state space models and direct multi-step predictors for linear
predictive control. We provide a tutorial exposition for both model structures to solve the …

As a useful and efficient alternative to generic model-based control scheme, data-driven
predictive control is subject to bias-variance trade-off and is known to not perform desirably …

The data-driven model predictive control (MPC) approach has been an effective tool for
unknown constrained systems. However, most of the existing designs rely on the prior …