Identification in Dynamic Networks

TL;DR

This paper reviews the current state and recent developments in system identification for linear dynamic networks, emphasizing the need for data-driven models in interconnected and structured systems for control and optimization.

Contribution

It provides an overview of classical and modern identification methods tailored for dynamic networks, highlighting new research directions in the field.

Findings

Classical prediction error methods are adapted for network systems.

Structured and interconnected systems pose unique identification challenges.

The paper highlights emerging research questions in dynamic network identification.

Abstract

System identification is a common tool for estimating (linear) plant models as a basis for model-based predictive control and optimization. The current challenges in process industry, however, ask for data-driven modelling techniques that go beyond the single unit/plant models. While optimization and control problems become more and more structured in the form of decentralized and/or distributed solutions, the related modelling problems will need to address structured and interconnected systems. An introduction will be given to the current state of the art and related developments in the identification of linear dynamic networks. Starting from classical prediction error methods for open-loop and closed-loop systems, several consequences for the handling of network situations will be presented and new research questions will be highlighted.