Self-adaptive piezoelectric vibration absorber with semi-passive tunable resonant shunts
This paper proposes a novel self-adaptive strategy to control piezoelectric vibration absorbers (PVA) using a semi-passive resonant shunt with tunable inductance for vibration attenuation of harmonically excited structures. The tunable inductor is realized using ferrite cores and its inductance is c...
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Veröffentlicht in: | Journal of sound and vibration 2024-08, Vol.583, p.118424, Article 118424 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | This paper proposes a novel self-adaptive strategy to control piezoelectric vibration absorbers (PVA) using a semi-passive resonant shunt with tunable inductance for vibration attenuation of harmonically excited structures. The tunable inductor is realized using ferrite cores and its inductance is controlled by means of the air gap effect between the cores using piezoelectric stack actuators. This device allows the control of the resonance frequency of the shunt circuit. The adaptive resonant shunt leverages the effect of antiresonance resulting from the electromechanical coupling of the structure with a resonant shunt with low electrical damping to attenuate the vibration of the harmonically excited structure. A machine learning control method based on a Gaussian process regression model is used to drive the tunable inductance based on minimizing the time-averaged RMS response of the structure. The experimental application of the proposed strategy is illustrated in an application to attenuate a single-mode of a simplified aircraft fuselage structure. A reduction of about 30% in the maximum vibration amplitude is observed by comparing the self-adaptive resonant circuit and a traditional resonant circuit designed based on the equal-peaks method.
•Self-adaptive piezoelectric vibration absorber for vibration attenuation in harmonically excited structures.•A novel semi-passive tunable resonant shunt based on airgap control between ferrite cores.•Supervised machine learning control algorithm using the Gaussian Process regression model. |
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ISSN: | 0022-460X 1095-8568 |
DOI: | 10.1016/j.jsv.2024.118424 |