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| Presentation | 2020-01-27 14:45 Neural ordinary differential equations-based static output feedback stabilization Koki Kobayashi (NAIST), Masaki Ogura (Osaka Univ), Masako Kishida (NII), Wadayama Tadashi (NITech), Kenji Sugimoto (NAIST)  RCC2019-73 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The static output-feedback stabilization problem is one of the basic controller synthesis problem in the systems and control theory． However, the problem reduces to a non-convex and nonlinear optimization problem and, therefore, is known to be difficult to solve． In this paper, we propose a data-driven methodology to design the feedback gain by using a deep-learning technique called Neural Ordinary Differential Equations． We compare the proposed method with a conventional and standard one in the literature based on linear matrix inequalities (LMIs)．We observe that the proposed method outperforms the existing methodology, thereby confirming the effectiveness of the proposed method. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Linear systems / static output feedback / stabilization / deep learning / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 395, RCC2019-73, pp. 19-22, Jan. 2020. | |
| Paper # | RCC2019-73 | |
| Date of Issue | 2020-01-20 (RCC) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | RCC2019-73 |
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| Conference Information | |
| Committee | RCC |
| Conference Date | 2020-01-27 - 2020-01-27 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Reliable Communication and Control, etc. |
| Paper Information | |
| Registration To | RCC |
| Conference Code | 2020-01-RCC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Neural ordinary differential equations-based static output feedback stabilization |
| Sub Title (in English) | |
| Keyword(1) | Linear systems |
| Keyword(2) | static output feedback |
| Keyword(3) | stabilization |
| Keyword(4) | deep learning |
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| 1st Author's Name | Koki Kobayashi |
| 1st Author's Affiliation | Nara Institute of Science and Technology (NAIST) |
| 2nd Author's Name | Masaki Ogura |
| 2nd Author's Affiliation | Osaka University (Osaka Univ) |
| 3rd Author's Name | Masako Kishida |
| 3rd Author's Affiliation | National Institute of Informatics (NII) |
| 4th Author's Name | Wadayama Tadashi |
| 4th Author's Affiliation | Nagoya Institute of Technology (NITech) |
| 5th Author's Name | Kenji Sugimoto |
| 5th Author's Affiliation | Nara Institute of Science and Technology (NAIST) |
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| Speaker | Author-1 |
| Date Time | 2020-01-27 14:45:00 |
| Presentation Time | 25 minutes |
| Registration for | RCC |
| Paper # | RCC2019-73 |
| Volume (vol) | vol.119 |
| Number (no) | no.395 |
| Page | pp.19-22 |
| #Pages | 4 |
| Date of Issue | 2020-01-20 (RCC) |
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