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

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

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

OriginalspracheEnglisch
Titel2023 IEEE 36th International Conference on Micro Electro Mechanical Systems (MEMS)
ErscheinungsortMünchen
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten538-541
Seitenumfang4
ISBN (elektronisch)978-1-6654-9308-6
ISBN (Print)978-1-6654-9309-3
PublikationsstatusVeröffentlicht - 2023
Peer-Review-StatusJa

Publikationsreihe

ReiheIEEE International Conference on Micro Electro Mechanical Systems (MEMS)
Band2023-January
ISSN1084-6999

Konferenz

Titel36th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2023
Dauer15 - 19 Januar 2023
StadtMunich
LandDeutschland

Externe IDs

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

Schlagworte

Schlagwörter

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