Proceedings of the 6th International Conference on Information Engineering for Mechanics and Materials

Research on acquisition and feature analysis of GMA status signal based on grating sensors

Authors
Ping Han, Kaichong Ma
Corresponding Author
Ping Han
Available Online November 2016.
DOI
https://doi.org/10.2991/icimm-16.2016.19How to use a DOI?
Keywords
Giant Magnetostrictive Actuator; BP neural network; Grating sensors.
Abstract

The new GMA experimental platform based on grating sensors and modeling method with BP neural network are proposed. The various parameters of GMA in the working process, such as driving voltage, prestress, displacement, are gathered and the effects are analyzed, then BP neural network is used to build the GMA model according with the real features of driving voltage and prestress. The model is verified by experiment with actual data, it can predict the actuating output of GMA greatly, and the predict error is in the range of 0-0.004nm.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 6th International Conference on Information Engineering for Mechanics and Materials
Series
Advances in Engineering Research
Publication Date
November 2016
ISBN
978-94-6252-244-2
ISSN
2352-5401
DOI
https://doi.org/10.2991/icimm-16.2016.19How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Ping Han
AU  - Kaichong Ma
PY  - 2016/11
DA  - 2016/11
TI  - Research on acquisition and feature analysis of GMA status signal based on grating sensors
BT  - Proceedings of the 6th International Conference on Information Engineering for Mechanics and Materials
PB  - Atlantis Press
SP  - 86
EP  - 90
SN  - 2352-5401
UR  - https://doi.org/10.2991/icimm-16.2016.19
DO  - https://doi.org/10.2991/icimm-16.2016.19
ID  - Han2016/11
ER  -