Multivariable parametric models are critical for designing, controlling, and optimizing the performance of engineered systems. The main aim of this paper is to develop a parametric identification strategy that delivers accurate and physically relevant models of multivariable systems using time-domain data. The introduced approach adopts an additive model structure, providing a parsimonious and interpretable representation of many physical systems, and applies a refined instrumental variable-based estimation algorithm. The developed identification method enables the estimation of multivariable parametric additive models in continuous time and is applicable to both open- and closed-loop systems. The performance of the estimator is demonstrated through numerical simulations and experimentally validated on a flexible beam system.
@article{arxiv.2504.01639,
title = {Identification of additive multivariable continuous-time systems},
author = {Maarten van der Hulst and Rodrigo González and Koen Classens and Nic Dirkx and Jeroen van de Wijdeven and Tom Oomen},
journal= {arXiv preprint arXiv:2504.01639},
year = {2025}
}