Excitation allocation for generic identifiability of linear dynamic networks with fixed modules

TL;DR

This paper extends the theory of excitation allocation in linear dynamic networks by incorporating fixed modules, showing that known modules reduce the number of external signals needed for generic identifiability, and provides a systematic allocation algorithm.

Contribution

It introduces a graphical approach that accounts for fixed modules, reducing excitation requirements and offering an algorithm for systematic signal allocation.

Findings

Fixed modules decrease the number of external signals needed.

The extended graphical approach effectively guides excitation allocation.

An algorithm for systematic excitation signal placement is proposed.

Abstract

Identifiability of linear dynamic networks requires the presence of a sufficient number of external excitation signals. The problem of allocating a minimal number of external signals for guaranteeing generic network identifiability has been recently addressed in the literature. Here we will extend that work by explicitly incorporating the situation that some network modules are known, and thus are fixed in the parametrized model set. The graphical approach introduced earlier is extended to this situation, showing that the presence of fixed modules reduces the required number of external signals. An algorithm is presented that allocates the external signals in a systematic fashion.