Structured identification of multivariable modal systems
arXiv:2510.10820v2 Announce Type: replace-cross Abstract: 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
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Abstract: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.
Comments: 23 pages, 13 figures
Subjects:
Systems and Control (eess.SY); Signal Processing (eess.SP)
Cite as: arXiv:2510.10820 [eess.SY]
(or arXiv:2510.10820v2 [eess.SY] for this version)
https://doi.org/10.48550/arXiv.2510.10820
arXiv-issued DOI via DataCite
Journal reference: journal = {Mechanical Systems and Signal Processing}, volume = {247}, pages = {113948}, year = {2026}, issn = {0888-3270},
Related DOI:
https://doi.org/10.1016/j.ymssp.2026.113948
DOI(s) linking to related resources
Submission history
From: Maarten Van Der Hulst [view email] [v1] Sun, 12 Oct 2025 22:06:16 UTC (4,760 KB) [v2] Tue, 31 Mar 2026 11:33:53 UTC (3,211 KB)
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