Statistically Optimal Structured Additive MIMO Continuous-time System Identification Article Swipe

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Rodrigo A. González , Maarten van der Hulst , Koen Classens , Tom Oomen ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2505.14169

Many applications in mechanical, acoustic, and electronic engineering require estimating complex dynamical models, often represented as additive multi-input multi-output (MIMO) transfer functions with structural constraints. This paper introduces a two-stage procedure for estimating structured additive MIMO models, where structural constraints are enforced through a weighted nonlinear least-squares projection of the parameter vector initially estimated using a recently developed refined instrumental variables algorithm. The proposed approach is shown to be consistent and asymptotically efficient in open-loop scenarios. In closed-loop settings, it remains consistent despite potential noise model misspecification and achieves minimum covariance among all instrumental variable estimators. Extensive simulations are performed to validate the theoretical findings, and to show the efficacy of the proposed approach.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2505.14169
PDF: https://arxiv.org/pdf/2505.14169
OA Status: green
OpenAlex ID: https://openalex.org/W4415021918

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DOI: https://doi.org/10.48550/arxiv.2505.14169

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Title: Statistically Optimal Structured Additive MIMO Continuous-time System Identification

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-05-20

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Authors: Rodrigo A. González, Maarten van der Hulst, Koen Classens, Tom Oomen

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Landing page: https://arxiv.org/abs/2505.14169

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PDF URL: https://arxiv.org/pdf/2505.14169

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Cited by: 0

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