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  1. B200 工学部/工学研究科
  2. B200e 会議資料
  3. 国際会議

A framework for optimal gait generation via learning optimal control using virtual constraint

http://hdl.handle.net/2237/12079
http://hdl.handle.net/2237/12079
eebf6472-c5e2-4a56-b48f-54a66611ce68
名前 / ファイル ライセンス アクション
iros08.pdf iros08.pdf (1.2 MB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2009-08-25
タイトル
タイトル A framework for optimal gait generation via learning optimal control using virtual constraint
言語 en
著者 Satoh, Satoshi

× Satoh, Satoshi

WEKO 31084

en Satoh, Satoshi

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Fujimoto, Kenji

× Fujimoto, Kenji

WEKO 31085

en Fujimoto, Kenji

Search repository
Hyon, Sang-Ho

× Hyon, Sang-Ho

WEKO 31086

en Hyon, Sang-Ho

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 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008. IROS 2008. p.3426-3432.<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 This paper proposes an optimal gait generation framework using virtual constraint and learning optimal control. In this method, firstly, we add a constraint by a virtual potential energy to prevent the robot from falling. Secondly, we execute iterative learning control (ILC) to generate an optimal feedforward input. Thirdly, we execute iterative feedback tuning (IFT) to mitigate the strength of the virtual constraint automatically according to the progress of learning control. Consequently, it is expected to generate an optimal gait without constraint eventually. Although existing ILC frameworks require a lot of experimental data under the same initial condition, the proposed method does not need to repeat experiments under the same initial condition because the virtual constraint restricts the motion of the robot to a symmetric trajectory. Furthermore, it does not require the precise knowledge of the plant system. Finally, some numerical simulations demonstrate the effectiveness of the proposed method.
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 AM
出版タイプResource http://purl.org/coar/version/c_ab4af688f83e57aa
DOI
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1109/IROS.2008.465086
書誌情報 en : IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2008)

p. 3426-3432, 発行日 2008-09
フォーマット
application/pdf
著者版フラグ
値 author
URI
識別子 http://dx.doi.org/10.1109/IROS.2008.465086
識別子タイプ DOI
URI
識別子 http://hdl.handle.net/2237/12079
識別子タイプ HDL
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