Iterative Learning Control for Quasi-Static MEMS Mirror with Switching Operation

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Contributors

Abstract

This paper reports an iterative learning control (ILC) to compensate for the errors by the switching operation and the modeling inaccuracies for a quasi-static (QS) MEMS mirror. The modeling errors and uncertainties in dynamics with the switching operation between electrodes result in undesirable oscillations in beam positioning. A wideband frequency-domain ILC is proposed for a QS MEMS mirror with a flatness-based feedforward control. The improvement of the residual oscillations is demonstrated by reduced root mean square (RMS) errors for a 2 Hz and a 2-degree-amplitude sawtooth reference with a factor 69.9.

Details

Original languageEnglish
Title of host publication2023 IEEE 36th International Conference on Micro Electro Mechanical Systems (MEMS)
Place of PublicationMünchen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages538-541
Number of pages4
ISBN (electronic)978-1-6654-9308-6
ISBN (print)978-1-6654-9309-3
Publication statusPublished - 2023
Peer-reviewedYes

Publication series

SeriesIEEE International Conference on Micro Electro Mechanical Systems (MEMS)
Volume2023-January
ISSN1084-6999

Conference

Title36th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2023
Duration15 - 19 January 2023
CityMunich
CountryGermany

External IDs

ORCID /0000-0003-3259-4571/work/142249669

Keywords

Keywords

  • Electrostatic Actuation, Iterative Learning Control, Quasi-Static MEMS Mirror, Switching Operation