Data-driven sparse discovery of hysteresis models for piezoelectric actuators

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Abstract

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.

Original languageEnglish
Title of host publication2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC)
PublisherInstitute of Electrical and Electronics Engineers
Number of pages2
ISBN (Electronic)978-1-6654-6833-6
DOIs
Publication statusPublished - 14 Nov 2022
Event20th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2022 - Virtual/Online, Virtual, Denver, United States
Duration: 24 Oct 202226 Oct 2022
Conference number: 20
https://2022.ieeecefc.org

Conference

Conference20th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2022
Abbreviated titleCEFC 2022
Country/TerritoryUnited States
CityVirtual, Denver
Period24/10/2226/10/22
Internet address

Keywords

  • Hysteresis
  • machine learning
  • piezoelectric actu-ator
  • sparse regression

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