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  1. A500 情報学部/情報学研究科・情報文化学部・情報科学研究科
  2. A500e 会議資料
  3. 国際会議

Improvement of a Parallel Type Two-axial Actuator Controlled by a Multi-layered Neural Network

http://hdl.handle.net/2237/12135
http://hdl.handle.net/2237/12135
422ab4b5-d7e4-4fdb-b94b-dda6b70eea62
名前 / ファイル ライセンス アクション
MHS2008_SA_2_7.pdf MHS2008_SA_2_7.pdf (558.1 kB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2009-08-31
タイトル
タイトル Improvement of a Parallel Type Two-axial Actuator Controlled by a Multi-layered Neural Network
言語 en
著者 Esumi, Kazuya

× Esumi, Kazuya

WEKO 31283

en Esumi, Kazuya

Search repository
Ohka, Masahiro

× Ohka, Masahiro

WEKO 31284

en Ohka, Masahiro

Search repository
Sawamoto, Yasuhiro

× Sawamoto, Yasuhiro

WEKO 31285

en Sawamoto, Yasuhiro

Search repository
Matsukawa, Shiho

× Matsukawa, Shiho

WEKO 31286

en Matsukawa, Shiho

Search repository
Miyaoka, Tetsu

× Miyaoka, Tetsu

WEKO 31287

en Miyaoka, Tetsu

Search repository
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
権利
言語 en
権利情報 Copyright © 2008 IEEE. Reprinted from International Symposium on Micro-NanoMechatronics and Human Science, 2008. MHS 2008. p.255-260.<br/>This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.
抄録
内容記述 Our parallel typed two-axial actuator was composed of two bimorph piezoelectric elements and two small links connected by three joints. We formulated kinematics for the parallel typed two-axial actuator because the endpoint is controlled in the two-dimensional coordinate. Since relationship between applied voltage and displacement cause by the voltage shows a hysteresis loop in the bimorph piezoelectric element used as components of the two-axial actuator, we produce a control system for the two-axial actuator based on a multi-layered artificial neural network to compensate the hysteresis. The neural network is comprised of 4 neurons in the input layer, 10 neurons in the hidden layer and ones neuron in the output layer. The output neuron emits time derivative of voltage; two bits signal expressing increment or decrement condition is generated by two input neurons; one of the other two input neurons and the other calculate current values of voltage and displacement, respectively. In the learning process, the network learns the hysteresis including minor loops. In the verification test, the endpoint of the two-axial actuator traces the desired circular trajectory in the two-dimensional coordinate system. After learning hysteresis loops including minor loops, the neural network simulates these hysteresis phenomena with very high accuracy.
言語 en
内容記述タイプ Abstract
出版者
言語 en
出版者 IEEE
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
DOI
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1109/MHS.2008.4752459
書誌情報 en : International Symposium on Micro-NanoMechatronics and Human Science (MHS 2008)

p. 255-260, 発行日 2008-11
フォーマット
application/pdf
著者版フラグ
値 publisher
URI
識別子 http://dx.doi.org/10.1109/MHS.2008.4752459
識別子タイプ DOI
URI
識別子 http://hdl.handle.net/2237/12135
識別子タイプ HDL
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