In this paper, a method to represent every input-output trajectory of a continuous-time linear
system in terms of previously collected data is presented. This corresponds to a continuous …

The article investigates the security time-varying formation control problem when the
communication network of multi-agent systems (MASs) suffers from Denial-of-Service (DoS) …

In this paper, a robust data-driven moving horizon estimation (MHE) scheme for linear time-
invariant discrete-time systems is introduced. The scheme solely relies on offline collected …

This paper addresses the joint state estimation and control problems for unknown linear time-
invariant systems subject to both process and measurement noise. The aim is to redesign …

This article is concerned with the secure state estimation problem of cyber-physical systems
(CPSs) in the presence of sparse sensor attacks. Considering that such a problem is a …

Studying structural properties of linear dynamical systems through invariant subspaces is
one of the key contributions of the geometric approach to system theory. In general, a model …

This paper addresses the problem of unknown input estimation for a class of nonlinear
systems with mixed nonlinear terms, namely Linear Parameter Varying (LPV) parts and …

This paper introduces a novel parameterization to characterize unknown linear time-
invariant systems using noisy data. The presented parameterization describes exactly the …

This letter proposes a data-driven input reconstruction method from outputs (IRO) based on
the Willems' Fundamental Lemma. Given only output measurements, the unknown …

In the field of model predictive control, the Data-enabled Predictive Controller (DeePC)
offers direct predictive control, bypassing traditional modeling. However, challenges emerge …