Data-driven sparse discovery of hysteresis models for piezoelectric actuators

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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Samenvatting

This paper proposes a new approach to model hysteresis in piezoelectric actuators based on recent advances in sparsity promoting machine learning methods. While sparse regression has successfully modelled several phenomena in sci-ence and engineering, its performance in modelling the nonlinear hysteresis behaviour in piezoelectric materials is still unexplored. This study applies the sequential threshold least-squares (STLSQ) algorithm to discover the governing hysteresis expressed in the form of a dynamical system. It is found that a parsimonious model governing the dynamics is extracted from a large candidate library, which predicts the hysteresis efficiently with less than le-3 relative percent error, demonstrating the algorithm's efficiency.

Originele taal-2Engels
Titel2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC)
UitgeverijInstitute of Electrical and Electronics Engineers
Aantal pagina's2
ISBN van elektronische versie978-1-6654-6833-6
DOI's
StatusGepubliceerd - 14 nov. 2022
Evenement20th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2022 - Virtual/Online, Virtual, Denver, Verenigde Staten van Amerika
Duur: 24 okt. 202226 okt. 2022
Congresnummer: 20
https://2022.ieeecefc.org

Congres

Congres20th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2022
Verkorte titelCEFC 2022
Land/RegioVerenigde Staten van Amerika
StadVirtual, Denver
Periode24/10/2226/10/22
Internet adres

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