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dc.contributor.author | Moriyasu, Ryuta | en |
dc.contributor.author | Ikeda, Taro | en |
dc.contributor.author | Kawaguchi, Sho | en |
dc.contributor.author | Kashima, Kenji | en |
dc.contributor.alternative | 森安, 竜大 | ja |
dc.contributor.alternative | 池田, 太郎 | ja |
dc.contributor.alternative | 川口, 翔 | ja |
dc.contributor.alternative | 加嶋, 健司 | ja |
dc.date.accessioned | 2021-07-09T07:55:04Z | - |
dc.date.available | 2021-07-09T07:55:04Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://hdl.handle.net/2433/264260 | - |
dc.description.abstract | This paper aims to improve the reliability of optimal control using models constructed by machine learning methods. Optimal control problems based on such models are generally non-convex and difficult to solve online. In this paper, we propose a model that combines the Hammerstein-Wiener model with input convex neural networks, which have recently been proposed in the field of machine learning. An important feature of the proposed model is that resulting optimal control problems are effectively solvable exploiting their convexity and partial linearity while retaining flexible modeling ability. The practical usefulness of the method is examined through its application to the modeling and control of an engine airpath system. | en |
dc.language.iso | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en |
dc.rights | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en |
dc.rights | This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 | en |
dc.subject | Model predictive control | en |
dc.subject | Machine learning | en |
dc.subject | Convex optimization | en |
dc.subject | Input convex neural network | en |
dc.title | Structured Hammerstein-Wiener model learning for model predictive control | en |
dc.type | journal article | - |
dc.type.niitype | Journal Article | - |
dc.identifier.jtitle | IEEE Control Systems Letters | en |
dc.identifier.volume | 6 | - |
dc.identifier.spage | 397 | - |
dc.identifier.epage | 402 | - |
dc.relation.doi | 10.1109/lcsys.2021.3077201 | - |
dc.textversion | author | - |
dc.address | Toyota Central R&D Labs | en |
dc.address | Toyota Central R&D Labs | en |
dc.address | Toyota Industries Coorporation | en |
dc.address | Graduate School of Informatics, Kyoto University | en |
dcterms.accessRights | open access | - |
dc.identifier.eissn | 2475-1456 | - |
出現コレクション: | 学術雑誌掲載論文等 |
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