2020 Fiscal Year Final Research Report
Data-driven model reduction of nonlinear complex systems
| Project/Area Number |
19K23517
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Multi-year Fund |
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| Review Section |
0302:Electrical and electronic engineering and related fields
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| Research Institution | Hiroshima University |
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Principal Investigator |
Kawano Yu 広島大学, 先進理工系科学研究科(工), 准教授 (40743034)
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| Project Period (FY) |
2019-08-30 – 2021-03-31
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| Keywords | モデル低次元化 / 非線形システム / 大規模システム / 単調システム / DCゲイン / グラミアン |
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| Outline of Final Research Achievements |
To extract essential components for analysis or control design from nonlinear large-scale systems, we have developed model reduction methods by data assimilation approaches, where data assimilation is a methodology for improving accuracy of estimating system behavior by integrating a system model and its historical data. In particular, we have constructed two model reduction method; one is for monotone network systems, and the other is to reduce computational complexity of model predictive control. The proposed methods are illustrated by gene regulatory networks and limit cycles.
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| Free Research Field |
制御工学
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| Academic Significance and Societal Importance of the Research Achievements |
IoT技術の躍進によるシステムの大規模ネットワーク化，要素技術の発展によるシステムモデルの精密化（非線形化）により，大規模非線形ネットワークとして幅広いシステムがモデル化される．記述能力が高いモデルであるものの，その複雑さから解析・制御系設計が難しいことが多い．本研究の成果は，このような問題を解決するための手助けになることが期待される．
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