By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-
driven predictive control (DPC) methods for LPV systems. In particular, we present output …

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 …

Model predictive control (MPC) is a control strategy widely used in industrial applications.
However, its implementation typically requires a mathematical model of the system being …

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 …

Model Predictive Control (MPC) is a powerful method for complex system regulation, but its
reliance on accurate models poses many limitations in real-world applications. Data-driven …

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 provide a theoretical analysis of closed-loop properties of a simple data-
driven model predictive control (MPC) scheme. The formulation does not involve any …

We introduce the notion of implicit predictors, which characterize the input-(state)-output
prediction behavior underlying a predictive control scheme, even if it is not explicitly …

We show that the explicit realization of data-driven predictive control (DPC) for linear
deterministic systems is more tractable than previously thought. To this end, we compare the …

Data-driven model predictive control (DD-MPC) based on Willems' Fundamental Lemma
has received much attention in recent years, allowing to control systems directly based on …