A Predictor Form State-Space Identification Algorithm Using Multivariate Linear Regression

被引：0
|
作者：
Cheng, Yiping [1 ]
机构：
[1] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Adv Control Syst Lab, Beijing 100044, Peoples R China
来源：
PROCEEDINGS OF THE 31ST CHINESE CONTROL CONFERENCE | 2012年
关键词：
State-Space Identification; Multivariate Linear Regression; Subspace Identification; Closed-Loop Identification;
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 describes a novel MIMO state-space identification algorithm that is based on multivariate linear regression rather than the usual subspace techniques such as orthogonal and oblique projection. We first estimate the Markov parameters of the predictor using multivariate regression, then the state sequence is estimated using singular value decomposition via an equation central to our approach, and finally the A, B, C, K matrices are computed again by multivariate regression. Our algorithm is in predictor form, so it is suitable for both open-and closed-loop cases. Numerical experiments show the accuracy of our algorithm.
引用
收藏
页码：1877 / 1880
页数：4
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