Please use this identifier to cite or link to this item:
|Title:||Neural network approach for linearization of the electrostatically actuated double-gimballed micromirror|
|Authors:||Zhou, G. |
Micromirror and macro modeling
|Citation:||Zhou, G., Cheo, K.K.L., Tay, F.E.H., Chau, F.S. (2004-08). Neural network approach for linearization of the electrostatically actuated double-gimballed micromirror. Analog Integrated Circuits and Signal Processing 40 (2) : 141-153. ScholarBank@NUS Repository. https://doi.org/10.1023/B:ALOG.0000032595.72995.62|
|Abstract:||In this paper, a hierarchical circuit based approach is used for the development of a reduced-order macro-model for a double-gimballed electrostatic torsional micromirror. The nonlinearity and cross-axis coupling of the micromirror subjected to the differential driving scheme are investigated using the proposed macro-model. The simulation results are used to train a feed-forward neural network which carries out a function approximation of the relation between the desired location and the required driving voltages. The trained neural network is then coded into MAST AHDL. System-level simulation of the micromirror together with the neural network is performed in the SABER™ simulator. It is found that using a feed-forward neural network, the linearity of the micromirror is greatly improved, the steady state of the cross-axis coupling is reduced to a negligible level and the transient response of the cross-axis coupling is also suppressed. This implies that introducing a feed-forward neural network would be useful to simplify the design of the feedback control system for the double-gimballed electrostatic torsional micromirror.|
|Source Title:||Analog Integrated Circuits and Signal Processing|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 18, 2018
WEB OF SCIENCETM
checked on Oct 10, 2018
checked on Oct 6, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.