Identification of Feedback and Nonlinear Systems by using Subspace Methods
| Project/Area Number |
20560428
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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 | Doshisha University |
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Principal Investigator |
KATAYAMA Tohru Doshisha University, 文化情報学部, 客員教授 (40026175)
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| Co-Investigator(Renkei-kenkyūsha) |
TANAKA Hideyuki 京都大学, 情報学研究科, 助教 (90303883)
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| Project Period (FY) |
2008 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2010: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2009: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2008: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | システム同定 / フィードバックシステム / 非線形システム / 部分空間同定法カルマンフィルタ / 非線形フィルタリング / 制御工学 / ウィーナーモデル / 部分空間同定法 / カルマンフィルタ / LQ分解 / 状態空間モデル / 部分空間法 / 分離最小2乗法 / 基底関数 |
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| Research Abstract |
Based on the orthogonal decomposition (ORT) method 1996, we have developed a new closed loop subspace identification algorithm under the assumption that measurable disturbances are available, and obtained successful numerical results for both simulated and industrial data. We have also considered an LQ decomposition based subspace identification method for identifying the stochastic part of a combined deterministic stochastic system, showing that the stochastic part is derived by using a single LQ decomposition under the assumption that the past horizon is sufficiently large. This result complements the well-known MOESP method, for which the identification of stochastic part has been ignored for many years. Concerning the identification of nonlinear dynamical systems, a method of identifying Wiener-Hammerstein systems, in which a static nonlinearity is sandwiched by two linear systems, is derived by using a method of alternately identifying Hammerstein system and a linear system, where the data driven local coordinate-based gradient method is used for minimizing output errors. The algorithm and numerical results for the benchmark problem are presented at the special session of SYSID 2009, Saint-Malo.
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Report
(4 results)
Research Products
(19 results)
