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A framework for optimal gait generation via learning optimal control using virtual constraint
http://hdl.handle.net/2237/12079
http://hdl.handle.net/2237/12079eebf6472-c5e2-4a56-b48f-54a66611ce68
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
iros08.pdf (1.2 MB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||
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| 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× Fujimoto, Kenji× Hyon, Sang-Ho |
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| 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 |
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| 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 | |||||
