2002 Fiscal Year Final Research Report Summary
Basic research on subspace identification method for practical use
| Project/Area Number |
11650451
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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 | Kyushu University |
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Principal Investigator |
WADA Kiyoshi Faculty of Information Science and Electrical Engineering, Professor, システム情報科学研究院, 教授 (60125127)
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| Co-Investigator(Kenkyū-buntansha) |
IMAI Jun Okayama University, Faculty of Engineering, Lecture, 工学部, 講師 (50243986)
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| Project Period (FY) |
1999 – 2002
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| Keywords | System Identification / MIMO System Identification / State Space Model / Subspace Identification / Recursive Algorithm / Structure Index |
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| Research Abstract |
For state space model identification of MIMO systems, the methods may be used for estimating impulse response from input-output data, then constructing Hankel matrix and deriving the state space model by Ho-Kalman's realization algorithm or Kung's realization algorithm based on singular value decomposition. But it is difficult to get highly accurate impulse response estimates of MIMO system from input-output data. Also, there is a problem that appropriate structure indices have to be decided for estimating the coefficient matrix of difference equation from input-output data and deriving the state space model.
Subspace-based state space system identification methods directly realize system matrices (A,B,C,D) of state space model from input-output data without intermediate expression, such as impulse responses or difference equations. The methods are essentially suitable for MIMO systems and therefore have attracted much attention as strong identification methods instead of traditional identification methods based on linear regression equations.
In this research, the following studies on subspace identification methods are carried out for practical use: 1) Packaging the software of various continuous-time system identification methods, and comparing various identification methods by this software package. 2) Developing a modeling method for robust control. 3) Proposing consistent estimation methods based on bias-compensation principal. 4) Presenting a recursive calculation algorithm of subspace identification. 5) Extension of subspace identification method to continuous-time systems and nonlinear systems.
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