nach oben

Erschienen in:

2024 | OriginalPaper | Buchkapitel

Physics-Enhanced Sparse Identification of Nonlinear Oscillator with Coulomb Friction

verfasst von : Christos Lathourakis, Alice Cicirello

Erschienen in: Advances in Nonlinear Dynamics, Volume III

Verlag: Springer Nature Switzerland

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The identification of the nonlinear governing equations of a single degree of freedom oscillator under harmonic excitation, including Coulomb friction damping, is investigated. The so-called RK4-SINDy approach is enhanced here by incorporating part of the known physics to handle the non-smooth friction law since state-of-the-art approaches fail to capture such nonlinearities. This simple, yet representative, case study is examined using both artificially generated noisy data and data obtained from an experimental setup, in both cases for continuous motion. The obtained results highlight the potential of this framework in nonlinear system identification, given sparsely collected corrupted data, and the benefits of incorporating already known system information.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Uncertainty Quantification in Parameter Estimation Using Physics-Integrated Machine Learning

Nächstes Kapitel Recycled Smart Devices for Real-Time Monitoring of Civil Infrastructures

Mendez, B.C., Botero, E., Romo, M.P.: A new friction law for sliding rigid blocks under cyclic loading. Soil Dyn. Earthq. Eng. 29(5), 874–882 (2009). https://doi.org/10.1016/j.soildyn.2008.09.004CrossRef

Cabboi, A., Putelat, T., Woodhouse, J.: The frequency response of dynamic friction: enhanced rate-and-state models. J. Mech. Phys. Solids 92, 210–236 (2016). https://doi.org/10.1016/j.jmps.2016.03.025CrossRef

Green, P.L., Worden, K., Sims, n.d.: On the identification and modelling of friction in a randomly excited energy harvester. J. Sound Vib. 332(19), 4696–4708 (2013). https://doi.org/10.1016/j.jsv.2013.04.024

Gutowski, P., Leus, M.: The effect of longitudinal tangential vibrations on friction and driving forces in sliding motion. Tribol. Int. 55, 108–118 (2012). https://doi.org/10.1016/j.triboint.2012.05.023CrossRef

Brunton, S.L., Proctor, J.L., Kutz, J.N.: Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc. Natl. Acad. Sci. 113(15), 3932–3937 (2016). https://doi.org/10.1073/pnas.1517384113MathSciNetCrossRef

Fehr, J., Kargl, A., Eschmann, H.: Identification of Friction Models for MPC-based Control of a PowerCube Serial Robot (2022). arXiv preprint arXiv:2203.10896. https://doi.org/10.48550/arXiv.2203.10896

Didonna, M., Stender, M., Papangelo, A., Fontanela, F., Ciavarella, M., Hoffmann, N.: Reconstruction of governing equations from vibration measurements for geometrically nonlinear systems. Lubricants 7(8), 64 (2019). https://doi.org/10.3390/lubricants7080064CrossRef

Ren, Y., Adams, C., Melz, T.: Uncertainty analysis and experimental validation of identifying the governing equation of an oscillator using sparse regression. Appl. Sci. 12(2), 747 (2022). https://doi.org/10.3390/app12020747CrossRef

Cabboi, A., Marino, L., Cicirello, A.: A comparative study between Amontons–Coulomb and Dieterich–Ruina friction laws for the cyclic response of a single degree of freedom system. Eur. J. Mech. A. Solids 96, 104737 (2022). https://doi.org/10.1016/j.euromechsol.2022.104737MathSciNetCrossRef

10.

Goyal, P., Benner, P.: Discovery of nonlinear dynamical systems using a Runge–Kutta inspired dictionary-based sparse regression approach. Proc. R. Soc. A 478(2262), 20 210 883 (2022). https://doi.org/10.1098/rspa.2021.0883

11.

Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning, vol. 1. Springer, New York (2001)

12.

Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B (Methodological) 58, 267–288 (1996). https://doi.org/10.1111/j.2517-6161.1996.tb02080.xMathSciNet

13.

Karniadakis, G.E., Kevrekidis, I.G., Lu, L., Perdikaris, P., Wang, S., Yang, L.: Physics-informed machine learning. Nat. Rev. Phys. 3(6), 422–440 (2021). https://doi.org/10.1038/s42254-021-00314-5CrossRef

14.

Marino, L., Cicirello, A.: Experimental investigation of a single-degree-of-freedom system with Coulomb friction. Nonlinear Dyn. 99(3), 1781–1799 (2020). https://doi.org/10.1007/s11071-019-05443-2CrossRef

15.

Dormand, J.R., Prince, P.J.: A family of embedded Runge-Kutta formulae. J. Comput. Appl. Math. 6(1), 19–26 (1980). https://doi.org/10.1016/0771-050X(80)90013-3MathSciNetCrossRef

16.

Corbetta, M.: Application of sparse identification of nonlinear dynamics for physics-informed learning. In: 2020 IEEE Aerospace Conference, pp. 1–8 (2020). https://doi.org/10.1109/AERO47225.2020.9172386

Titel: Physics-Enhanced Sparse Identification of Nonlinear Oscillator with Coulomb Friction
verfasst von: Christos Lathourakis
Alice Cicirello
Verlag: Springer Nature Switzerland
Buch: Advances in Nonlinear Dynamics, Volume III
Print ISBN: 978-3-031-50634-5

Electronic ISBN: 978-3-031-50635-2

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-50635-2_47

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht