50 citations · 89 across the 10 of their papers we have counts for
12 papers
Data-based system representations from irregularly measured data
Mohammad Alsalti, Ivan Markovsky, Victor G. Lopez +1
Non-parametric representations of dynamical systems based on the image of a Hankel matrix of data are extensively used for data-driven control. However, if samples of data are miss…
Data-Driven Output Matching of Output-Generalized Bilinear and Linear Parameter-Varying systems
Leander Hemelhof, Ivan Markovsky, Panagiotis Patrinos
There is a growing interest in data-driven control of nonlinear systems over the last years. In contrast to related works, this paper takes a step back and aims to solve the output…
Addition and intersection of linear time-invariant behaviors
Antonio Fazzi, Ivan Markovsky
We define and analyze the operations of addition and intersection of linear time-invariant systems in the behavioral setting, where systems are viewed as sets of trajectories rathe…
Bridging direct & indirect data-driven control formulations via regularizations and relaxations
Florian Dörfler, Jeremy Coulson, Ivan Markovsky
We discuss connections between sequential system identification and control for linear time-invariant systems, often termed indirect data-driven control, as well as a contemporary…
A gradient system approach for Hankel structured low-rank approximation
Antonio Fazzi, Nicola Guglielmi, Ivan Markovsky
Rank deficient Hankel matrices are at the core of several applications. However, in practice, the coefficients of these matrices are noisy due to e.g. measurements errors and compu…
Generalized algorithms for the approximate matrix polynomial GCD of reducing data uncertainties with application to MIMO system and control
A. Fazzi, N. Guglielmi, I. Markovsky
Computation of (approximate) polynomials common factors is an important problem in several fields of science, like control theory and signal processing. While the problem has been…