Identification of linear dynamical systems and machine learning

TL;DR

This paper reviews realization theory for system identification and introduces machine learning-inspired methods to enhance the process of identifying linear dynamical systems from input-output data.

Contribution

It combines classical realization theory with modern machine learning techniques to propose new methods for system identification.

Findings

Revisits classical realization theory concepts.

Develops new identification methods inspired by machine learning.

Highlights potential improvements in system identification accuracy.

Abstract

The topic of identification of dynamic systems, has been at the core of modern control , following the fundamental works of Kalman. Realization Theory has been one of the major outcomes in this domain, with the possibility of identifying a dynamic system from an input-output relationship. The recent development of machine learning concepts has rejuvanated interest for identification. In this paper, we review briefly the results of realization theory, and develop some methods inspired by Machine Learning concepts.