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Towards automated model order reduction and feedback control for nonlinear finite element models

  • Spanning applications from microsystems to passenger jets, finite element (FE) models play a crucial role in the design of a wide range of technical products. However, such models are not suited for applications at system-level or in control due to their large scale. While several schemes to obtain models and controllers based on FE models exist, they either require high-level access to the FE software, create black-box models without physical interpretation, or require specialist knowledge. This paper outlines a workflow to generate highly efficient models and appropriate controllers for nonlinear FE models at the push of a button. In a first step, model order reduction via proper orthogonal decomposition creates an accurate surrogate model of drastically smaller dimension. Nonlinearities are handled via the trajectory piecewise linear approximation (TPWL), maximizing compatibility with commercial FE software by exclusively relying on data produced by regular solutions. Complementing TPWL,Spanning applications from microsystems to passenger jets, finite element (FE) models play a crucial role in the design of a wide range of technical products. However, such models are not suited for applications at system-level or in control due to their large scale. While several schemes to obtain models and controllers based on FE models exist, they either require high-level access to the FE software, create black-box models without physical interpretation, or require specialist knowledge. This paper outlines a workflow to generate highly efficient models and appropriate controllers for nonlinear FE models at the push of a button. In a first step, model order reduction via proper orthogonal decomposition creates an accurate surrogate model of drastically smaller dimension. Nonlinearities are handled via the trajectory piecewise linear approximation (TPWL), maximizing compatibility with commercial FE software by exclusively relying on data produced by regular solutions. Complementing TPWL, gain-scheduling is deployed to establish a precise controller. The proposed workflow is demonstrated for a tunable prism, showcasing its efficacy.show moreshow less

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Metadaten
Author:Arwed Schütz, Michael OlbrichGND, Aliakbar Taghdiri, Christoph AmentORCiDGND, Tamara Bechtold
URN:urn:nbn:de:bvb:384-opus4-1210450
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/121045
ISSN:2405-8963OPAC
Parent Title (English):IFAC-PapersOnLine
Publisher:Elsevier BV
Place of publication:Amsterdam
Type:Article
Language:English
Year of first Publication:2025
Publishing Institution:Universität Augsburg
Release Date:2025/04/02
Volume:59
Issue:1
First Page:139
Last Page:144
DOI:https://doi.org/10.1016/j.ifacol.2025.03.025
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Ingenieurinformatik mit Schwerpunkt Regelungstechnik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Licence (German):CC-BY-NC-ND 4.0: Creative Commons: Namensnennung - Nicht kommerziell - Keine Bearbeitung (mit Print on Demand)