| Project/Area Number |
11450164
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Control engineering
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| Research Institution | The University of Tokyo |
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Principal Investigator |
KIMURA Hidenori Complexity Science & Eng., The University of Tokyo, Professor, 大学院・新領域創成化学研究科, 教授 (10029514)
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| Co-Investigator(Kenkyū-buntansha) |
YAMAMOTO Shigeru Osaka Univ., Systems & Human Science, Associate Professor, 大学院・基礎工学研究科, 助教授 (70220465)
OISHI Yasuaki Mathematical Informatics, The University of Tokyo, Assistant Professor, 大学院・情報理工学系研究科, 講師 (80272392)
TSUMURA Kouji Information Physics & Computing, The University of Tokyo, Associate Prof., 大学院・情報理工学系研究科, 助教授 (80241941)
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| Project Period (FY) |
1999 – 2001
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| Project Status |
Completed (Fiscal Year 2001)
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| Budget Amount *help |
¥6,700,000 (Direct Cost: ¥6,700,000)
Fiscal Year 2001: ¥1,900,000 (Direct Cost: ¥1,900,000)
Fiscal Year 2000: ¥1,900,000 (Direct Cost: ¥1,900,000)
Fiscal Year 1999: ¥2,900,000 (Direct Cost: ¥2,900,000)
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| Keywords | Model / System identification / 4SID Method / Complexity / Learning theory / Control Systems / Adaptive control / LPV control / 赤池情報量基準 / モデル集合同定 / サンプル複雑度 / モデル駆動制御 / 逆モデル / 運動制御 / ブレンディングコントロール / 内部モデル制御 / モデル集合 / 複雑度 / 情報量基準 / スイッチング制御 / 部分空間同定 / 熱交換器 / 線形パラメータ変動システム / 統計的学習理論 |
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| Research Abstract |
1 Real-Time Identification of Time-Varying Systems
We are successful in obtaining sequential version of the 4-SID method which enables us to track the parameter variation of the system. We have clarified some fundamental structure of sequential 4-SID method from the viewpoint of realization theory. This is the most remarkable result we have obtained in this research program.
2 Analysis and Quantification of Model Complexity and Its Relation to Learning
We have applied learning theory to identification of model set and obtained the lower and upper bound of the samples required for guaranteering a prescribed model accuracy. We have defined complexity of model set through this sample number bounds. We have pointed out that the prevailing stochastic analysis of model set identification connected with control has some drawback.
3 Control Algorithm for Linear Parameter Varying (LPV) Systems
We have obtained some fundamental results on this issue. H-infinity control structure and topological aspects of model set have been considered from the LPV point of view. Also, we have considered the application of the so-called just-in-time (JIT) modeling. However, we are unable to establish so far the solid control architecture and algorithm for LPV systems based on the results obtained in (1) and (2).
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