| Project/Area Number |
24560543
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Control engineering
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| Research Institution | Kyoto University |
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Principal Investigator |
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| Research Collaborator |
QUEVEDO Daniel E. Paderborn University
OSTERGAARD Jan Aalborg University
NESIC Dragan Melbourne University
MARTIN Clyde Texas Tech University
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| Project Period (FY) |
2012-04-01 – 2015-03-31
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| Project Status |
Completed (Fiscal Year 2014)
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| Budget Amount *help |
¥5,590,000 (Direct Cost: ¥4,300,000、Indirect Cost: ¥1,290,000)
Fiscal Year 2014: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2013: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2012: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | 最適制御 / スパースモデリング / スパース表現 / 圧縮センシング / スパース最適制御 / ネットワーク化制御 / 情報圧縮 / 安定性 / L1最適性 / L1最適化 / 安定化 / 量子化 |
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| Outline of Final Research Achievements |
In this research, I have applied the notion of sparsity, which has been an important research subject in signal processing and machine learning, to networked control. I have proposed a novel method of signal representation for networked control systems, which can achieve high efficient data compression of control signals in the presence of noise. Moreover, I have proposed a novel optimal control, called sparse optimal control, which is an extension of sparsity to continuous-time signals. Sparse optimal control is a control that has the minimum support length among all admissible control signals. This optimal control problem is highly non-convex and difficult to solve. For this problem, I have derived a simple sufficient condition for the equivalence between the sparse optimal control and the L1 optimal control that is a convex relaxation of the sparse optimal control.
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