Data-driven sparse discovery of hysteresis models for piezoelectric actuators

Abhishek Chandra; Mitrofan Curti; Koen Tiels; Elena A. Lomonova; Daniel M. Tartakovsky

doi:10.1109/CEFC55061.2022.9940793

Data-driven sparse discovery of hysteresis models for piezoelectric actuators

Abhishek Chandra, Mitrofan Curti, Koen Tiels, Elena A. Lomonova, Daniel M. Tartakovsky

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

9 Downloads (Pure)

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 language	English
Title of host publication	2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC)
Publisher	Institute of Electrical and Electronics Engineers
Number of pages	2
ISBN (Electronic)	978-1-6654-6833-6
DOIs	https://doi.org/10.1109/CEFC55061.2022.9940793
Publication status	Published - 14 Nov 2022
Event	20th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2022 - Virtual/Online, Virtual, Denver, United States Duration: 24 Oct 2022 → 26 Oct 2022 Conference number: 20 https://2022.ieeecefc.org

Conference

Conference	20th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2022
Abbreviated title	CEFC 2022
Country/Territory	United States
City	Virtual, Denver
Period	24/10/22 → 26/10/22
Internet address	https://2022.ieeecefc.org

Keywords

Hysteresis
machine learning
piezoelectric actu-ator
sparse regression

Access to Document

10.1109/CEFC55061.2022.9940793

Cite this

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title = "Data-driven sparse discovery of hysteresis models for piezoelectric actuators",

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.",

keywords = "Hysteresis, machine learning, piezoelectric actu-ator, sparse regression",

author = "Abhishek Chandra and Mitrofan Curti and Koen Tiels and Lomonova, {Elena A.} and Tartakovsky, {Daniel M.}",

year = "2022",

month = nov,

day = "14",

doi = "10.1109/CEFC55061.2022.9940793",

language = "English",

booktitle = "2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC)",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "20th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2022, CEFC 2022 ; Conference date: 24-10-2022 Through 26-10-2022",

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}

Chandra, A , Curti, M , Tiels, K , Lomonova, EA & Tartakovsky, DM 2022, Data-driven sparse discovery of hysteresis models for piezoelectric actuators. in 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC)., 9940793, Institute of Electrical and Electronics Engineers, 20th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2022, Virtual, Denver, Colorado, United States, 24/10/22. https://doi.org/10.1109/CEFC55061.2022.9940793

Data-driven sparse discovery of hysteresis models for piezoelectric actuators. / Chandra, Abhishek ; Curti, Mitrofan ; Tiels, Koen et al.
2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC). Institute of Electrical and Electronics Engineers, 2022. 9940793.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Data-driven sparse discovery of hysteresis models for piezoelectric actuators

AU - Chandra, Abhishek

AU - Curti, Mitrofan

AU - Tiels, Koen

AU - Lomonova, Elena A.

AU - Tartakovsky, Daniel M.

N1 - Conference code: 20

PY - 2022/11/14

Y1 - 2022/11/14

N2 - 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.

AB - 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.

KW - Hysteresis

KW - machine learning

KW - piezoelectric actu-ator

KW - sparse regression

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M3 - Conference contribution

AN - SCOPUS:85143063424

BT - 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC)

PB - Institute of Electrical and Electronics Engineers

T2 - 20th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2022

Y2 - 24 October 2022 through 26 October 2022

ER -

Data-driven sparse discovery of hysteresis models for piezoelectric actuators

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