Iterative Learning Control for Quasi-Static MEMS Mirror with Switching Operation
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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
Originalsprache | Englisch |
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Titel | 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems (MEMS) |
Erscheinungsort | München |
Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
Seiten | 538-541 |
Seitenumfang | 4 |
ISBN (elektronisch) | 978-1-6654-9308-6 |
ISBN (Print) | 978-1-6654-9309-3 |
Publikationsstatus | Veröffentlicht - 2023 |
Peer-Review-Status | Ja |
Publikationsreihe
Reihe | IEEE International Conference on Micro Electro Mechanical Systems (MEMS) |
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Band | 2023-January |
ISSN | 1084-6999 |
Konferenz
Titel | 36th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2023 |
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Dauer | 15 - 19 Januar 2023 |
Stadt | Munich |
Land | Deutschland |
Externe IDs
ORCID | /0000-0003-3259-4571/work/142249669 |
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Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- Electrostatic Actuation, Iterative Learning Control, Quasi-Static MEMS Mirror, Switching Operation