Abstract
This paper proposes a subspace identification method involving a priori knowledge characterized as the moment of the transfer function. First, it is shown that any moment defined on the complex plane is expressed in terms of the solution to a Sylvester matrix equation with real-valued coefficients. Then, incorporating the Sylvester equation with the conventional subspace method, we formulate a moment-constrained subspace identification problem. Application of a proper weight reduces the constrained identification problem to a non-constrained quadratic programming. Finally, we propose a two-stage identification procedure: First, in the preliminary identification stage, the moments are estimated by using a specific input signal. Then, in the main identification stage, a state-space model is constructed based on the proposed identification method using the measured input-output data and the estimated moments. The effectiveness of the two-stage procedure is shown in a numerical simulation.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IEEE Conference on Decision and Control |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2731-2736 |
| Number of pages | 6 |
| Volume | 2016-February |
| ISBN (Print) | 9781479978861 |
| DOIs | |
| Publication status | Published - 2016 Feb 8 |
| Event | 54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan Duration: 2015 Dec 15 → 2015 Dec 18 |
Other
| Other | 54th IEEE Conference on Decision and Control, CDC 2015 |
|---|---|
| Country | Japan |
| City | Osaka |
| Period | 15/12/15 → 15/12/18 |
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ASJC Scopus subject areas
- Control and Systems Engineering
- Modelling and Simulation
- Control and Optimization
Cite this
Moment-constrained subspace identification using a priori knowledge. / Inoue, Masaki; Matsubayashi, Ayaka; Adachi, Shuichi.
Proceedings of the IEEE Conference on Decision and Control. Vol. 2016-February Institute of Electrical and Electronics Engineers Inc., 2016. p. 2731-2736 7402629.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Moment-constrained subspace identification using a priori knowledge
AU - Inoue, Masaki
AU - Matsubayashi, Ayaka
AU - Adachi, Shuichi
PY - 2016/2/8
Y1 - 2016/2/8
N2 - This paper proposes a subspace identification method involving a priori knowledge characterized as the moment of the transfer function. First, it is shown that any moment defined on the complex plane is expressed in terms of the solution to a Sylvester matrix equation with real-valued coefficients. Then, incorporating the Sylvester equation with the conventional subspace method, we formulate a moment-constrained subspace identification problem. Application of a proper weight reduces the constrained identification problem to a non-constrained quadratic programming. Finally, we propose a two-stage identification procedure: First, in the preliminary identification stage, the moments are estimated by using a specific input signal. Then, in the main identification stage, a state-space model is constructed based on the proposed identification method using the measured input-output data and the estimated moments. The effectiveness of the two-stage procedure is shown in a numerical simulation.
AB - This paper proposes a subspace identification method involving a priori knowledge characterized as the moment of the transfer function. First, it is shown that any moment defined on the complex plane is expressed in terms of the solution to a Sylvester matrix equation with real-valued coefficients. Then, incorporating the Sylvester equation with the conventional subspace method, we formulate a moment-constrained subspace identification problem. Application of a proper weight reduces the constrained identification problem to a non-constrained quadratic programming. Finally, we propose a two-stage identification procedure: First, in the preliminary identification stage, the moments are estimated by using a specific input signal. Then, in the main identification stage, a state-space model is constructed based on the proposed identification method using the measured input-output data and the estimated moments. The effectiveness of the two-stage procedure is shown in a numerical simulation.
UR - http://www.scopus.com/inward/record.url?scp=84962016055&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962016055&partnerID=8YFLogxK
U2 - 10.1109/CDC.2015.7402629
DO - 10.1109/CDC.2015.7402629
M3 - Conference contribution
AN - SCOPUS:84962016055
SN - 9781479978861
VL - 2016-February
SP - 2731
EP - 2736
BT - Proceedings of the IEEE Conference on Decision and Control
PB - Institute of Electrical and Electronics Engineers Inc.
ER -