  When identifying electrical, mechanical, or biological systems, parametric
continuous-time identification methods can lead to interpretable and
parsimonious models when the model structure aligns with the physical
properties of the system. Traditional linear system identification may not
consider the most parsimonious model when relying solely on unfactored transfer
functions, which typically result from standard direct approaches. This paper
presents a novel identification method that delivers additive models for both
open and closed-loop setups. The estimators that are derived are shown to be
generically consistent, and can admit the identification of marginally stable
additive systems. Numerical simulations show the efficacy of the proposed
approach, and its performance in identifying a modal representation of a
flexible beam is verified using experimental data.
