Data-driven dynamic interpolation and approximation: System theory without transfer function and state space representations (Prof. Ivan Markovsky, Vrije Universiteit Brussel)
Systems & Control Seminar
Abstract
Using the behavioral approach to system theory, we derive a nonparametric representation of linear time-invariant systems based on Hankel matrices constructed from data. The data-driven representation leads to new system identification, signal processing, and control methods. In this talk, we show howthe data-driven representation can be used for solving missing data estimation problems. The theory leads to algorithms that are general---can deal simultaneously with missing, exact, and noisy data of multivariable systems---and simple---require basic linear algebra operations only. The results open a practical computational way of doing system theory and signal processing directly from data without identification of a transfer function or a state space representation and doing model-based design. This is joint work with Florian Dorfler (ETH-Ztirich).
Biographical information
Ivan Markovsky is an ICREA professor at the International Centre for Numerical Methodsin Engineering, Barcelona. He received his Ph.D. degree in Electrical Engineering from the Katholieke Universiteit Leuven in February 2005. From 2006 to 2012 he wasan Assistant Professor at the School of Electronics and Computer Science of the University of Southampton and from 2012 to 2022 an Associate Professor at the Vrije Universiteit Brussel. He is a recipient of an ERC starting grant “Structured low-rank approximation: Theory, algorithms, and applications" 2010--2015, Householder Prize honorable mention 2008, and research mandate bythe Vrije Universiteit Brussel research council 2012--2022. His main research interests are computational methods for system theory, identification, and data-driven control in the behavioral setting.
Date
10. Nov. 202216:00 - 17:00
