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 …

This survey presents recent research on determining control-theoretic properties and
designing controllers with rigorous guarantees using semidefinite programming and for …

Data-driven controllers design is an important research problem, in particular when data is
corrupted by the noise. In this paper, we propose a data-driven min-max model predictive …

This paper studies uncertainty set estimation for unknown linear systems. Uncertainty sets
are crucial for the quality of robust control since they directly influence the conservativeness …

We present a stochastic constrained output-feedback data-driven predictive control scheme
for linear time-invariant systems subject to bounded additive disturbances. The approach …

In this paper we develop a data-driven approach for supervisory control of discrete-event
systems (DES). We consider a setup in which models of DES to be controlled are unknown …

Willems' fundamental lemma has recently received an impressive amount of attention in the
(data-driven) control community. In this paper, we formulate a frequency-domain equivalent …

Factors like improved data availability and increasing system complexity have sparked
interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive …

The Realization Independent Iterative Rational Krylov Algorithm (TF-IRKA) is a frequency-
based data-driven reduced order modeling (DDROM) method that constructs $\mathcal H_2 …

The objective of model reference control is to design a controller that regulates the system's
behavior so as to match a specified reference model. This paper investigates necessary and …