Closed-Loop Subspace Identification with Long Data based on Nuclear Norm Minimization

被引：0

作者：

Sugie, Toshiharu ^{[1
]}

Inoue, Kosei ^{[1
]}

Maruta, Ichiro ^{[2
]}

机构：

[1] Kyoto Univ, Dept Syst Sci, Sakyo Ku, Kyoto 6068501, Japan

[2] Kyoto Univ, Dept Aeronaut & Astronaut, Nishikyo Ku, Kyoto 6158510, Japan

来源：

2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC) | 2017年

关键词：

SYSTEM-IDENTIFICATION; RANK MINIMIZATION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes a subspace method for closed loop identification, which is aimed to provide low order system models in the presence of colored noises without the knowledge of feedback controllers. The method utilizes the long I/O data of the target system in order to be noise tolerant. First, it compresses the long data by projecting them to an appropriate subspace, then obtains a low order model based on the compressed data by exploiting the nuclear norm minimization. Its effectiveness is demonstrated through numerical simulations.

引用

页数：5

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