Physically interpretable models are essential for next-generation industrial systems, as these representations enable effective control, support design validation, and provide a foundation for monitoring strategies. The aim of this paper is to develop a system identification framework for estimating modal models of complex multivariable mechanical systems from frequency response data. To achieve this, a two-step structured identification algorithm is presented, where an additive model is first estimated using a refined instrumental variable method and subsequently projected onto a modal form. The developed identification method provides accurate, physically-relevant, minimal-order models, for both generally-damped and proportionally damped modal systems. The effectiveness of the proposed method is demonstrated through experimental validation on a prototype wafer-stage system, which features a large number of spatially distributed actuators and sensors and exhibits complex flexible dynamics.
@article{arxiv.2510.10820,
title = {Structured identification of multivariable modal systems},
author = {Maarten van der Hulst and Rodrigo A. González and Koen Classens and Paul Tacx and Nick Dirkx and Jeroen van de Wijdeven and Tom Oomen},
journal= {arXiv preprint arXiv:2510.10820},
year = {2026}
}