| Project/Area Number |
13650485
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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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 | KYOTO UNIVERSITY |
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Principal Investigator |
KATAYAMA Tohru Kyoto University, Graduate School of Informatics, Professor, 情報学研究科, 教授 (40026175)
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| Co-Investigator(Kenkyū-buntansha) |
TANAKA Hideyuki Kyoto University, Graduate School of Informatics, Research Associate, 情報学研究科, 助手 (90303883)
TAKABE Kiyotsugu Kyoto University, Graduate School of Informatics, Associate Professor, 情報学研究科, 助教授 (30236343)
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| Project Period (FY) |
2001 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥3,200,000 (Direct Cost: ¥3,200,000)
Fiscal Year 2002: ¥1,600,000 (Direct Cost: ¥1,600,000)
Fiscal Year 2001: ¥1,600,000 (Direct Cost: ¥1,600,000)
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| Keywords | Subspace methods / Stochastic realization / Feedback control systems / System identification / OR decomposition / Singular value decomposition / SVD / 直交分解 / QR法 |
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| Research Abstract |
Identification problems for linear systems operating in closed-loop have received much interest in the literature, since closed-loop experiments are necessary if the open-loop plant is unstable, or the feedback is an inherent mechanism of a system. We have obtained the following results.
1. We have shown that the identification of deterministic part of a closed loop system is completely decoupled from that of stochastic part by a preliminary orthogonal decomposition of the joint input-output processes into stochastic and deterministic components. Also, we have derived a method for obtaining the plant transfer function by applying a subspace identification method to the deterministic component of the joint process.
2. We have analyzed two closed loop identification methods : the one is the two-stage prediction error method based on the least-squares method, and the other is a subspace identification method based on the orthogonal decomposition. A bootstrap method is applied to estimate the standard error of identified models. Simulation results are included to show that accurate estimates of standard deviation are obtained.
3. Under the assumption that one of the exogenous inputs is purely-deterministic and the other is purely non-deterministic, we have developed a subspace method for identifying a closed loop system based on the realization results for stochastic systems with exogenous inputs. The present method can be used for identification of transfer functions of plant and controller. Numerical results show that the orthogonal decomposition-based algorithm gives better results over other subspace identification algorithms.
4. Subspace identification methods are applied to a PID control system and a distillation column in a chemical industry with considerable success. Some results are presented at International Symposium on Advance Control of Industrial Processes, at Kumamoto (2002).
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