Macho, M., Yoo, H. W., Schroedter, R., & Schitter, G. (2023). Iterative Learning Control for Quasi-Static MEMS Mirror with Switching Operation. In 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems (MEMS) (pp. 538–541). IEEE. https://doi.org/10.1109/MEMS49605.2023.10052637
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.
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Projekttitel:
Vielseitige Technologieplattform von MEMS-Scansystemen für sicherheitssteigernde Automobilanwendungen: 884345 (FFG - Österr. Forschungsförderungs- gesellschaft mbH)
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Forschungsschwerpunkte:
Mathematical and Algorithmic Foundations: 50% Sensor Systems: 50%
