{"ID":2856914,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.10820","arxiv_id":"2510.10820","title":"Structured identification of multivariable modal systems","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.","short_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 com...","url_abs":"https://arxiv.org/abs/2510.10820","url_pdf":"https://arxiv.org/pdf/2510.10820v2","authors":"[\"Maarten van der Hulst\",\"Rodrigo A. González\",\"Koen Classens\",\"Paul Tacx\",\"Nick Dirkx\",\"Jeroen van de Wijdeven\",\"Tom Oomen\"]","published":"2025-10-12T22:06:16Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"eess.SP\"]","methods":"[]","has_code":false}