Partial system identification and distributed optimization of control systems
| Project/Area Number |
16K14284
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Control engineering/System engineering
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| Research Institution | Kyoto University |
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Principal Investigator |
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | システム同定 / 閉ループ同定 / 線形システム / 非線形システム / マルチエージェント / 制御工学 / システムモデリング / 最適化 |
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| Outline of Final Research Achievements |
This study is concerned with system identification and control for large scale systems which consist of many subsystems through network connection. First, two types of subsystem identification methods are proposed. One is to exploit nuclear norm minimization and projection in the framework of subspace system identification, which does not require any information on other subsystems nor noise models. The other is a new type of output error prediction which uses the output of the model stabilized by a virtual controller, which is applicable to a class of nonlinear systems. Furthermore, a new scalable control method to achieve formation is proposed. The effectiveness of these methods are confirmed by numerical examples.
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| Academic Significance and Societal Importance of the Research Achievements |
電力のスマートグリッドに代表される大規模なネットワーク結合型システムを対象とした制御が現在では不可欠となっている．その制御の基礎となるのはシステムのモデル構築であるが，このような大規模システムのモデルを入出力データから直接構成することは難しい．本研究では，部分サブシステムに着目し，その入出力データから直接にサブシステムをモデル化する手法を提案したものである．非線形システムにも対応可能である．
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Report
(4 results)
Research Products
(14 results)
