Recursive closed-loop subspace identification based on innovation estimation

被引：0

作者：

Yu, Miao ^{[1
]}

Wang, You-Yi ^{[1
]}

Guo, Ge ^{[1
,2
]}

Liu, Jian-Chang ^{[2
,3
]}

机构：

[1] School of Control Engineering, Northeastern University at Qinhuangdao, Hebei, Qinhuangdao

[2] State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Liaoning, Shenyang

[3] College of Information Science and Engineering, Northeastern University, Liaoning, Shenyang

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2025年 / 42卷 / 02期

基金：

中国国家自然科学基金;

关键词：

innovation estimation; least squares approximations; recursive closed-loop identification; subspace identification;

D O I：

10.7641/CTA.2024.30826

中图分类号：

学科分类号：

摘要：

A recursive closed-loop subspace identification method based on innovation estimation is proposed for closed-loop control systems. Firstly, the influence of past noise on future input in the closed-loop control system is eliminated by innovation estimation, and the estimation of subspace matrix parameters is obtained. Secondly, the recursive method of least squares is used to eliminate the influence of noise, which realizes the parameter estimation of the lower triangle Toeplitz matrix. Finally, based on the Kung’s realization algorithm, the system matrices are extracted from Hankel matrix constructed by the estimated parameters. The simulation results show the effectiveness and superiority of the proposed method. © 2025 South China University of Technology. All rights reserved.

引用

页码：245 / 252

页数：7

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