# On Cyclic Finite-State Approximation of Data-Driven Systems

## TL;DR

This paper introduces new theoretical and computational methods for finite-state approximation of data-driven systems, aiming to preserve structure in system identification and control applications.

## Contribution

It presents novel techniques for constrained approximation of data-driven systems, combining theoretical development with numerical implementations for control and simulation.

## Key findings

- Effective finite-state approximations demonstrated in electrical signal models
- New algorithms improve structure preservation in data-driven system modeling
- Numerical results validate the proposed methods' applicability

## Abstract

In this document, some novel theoretical and computational techniques for constrained approximation of data-driven systems, are presented. The motivation for the development of these techniques came from structure-preserving matrix approximation problems that appear in the fields of system identification and model predictive control, for data-driven systems and processes. The research reported in this document is focused on finite-state approximation of data-driven systems. Some numerical implementations of the aforementioned techniques in the simulation and model predictive control of some generic data-driven systems, that are related to electrical signal transmission models, are outlined.