Subspace Identification of Closed-Loop EIV System Based on Instrumental Variables Using Orthoprojection

被引：0
作者：
Youfeng Li
Zenggang Xiong
Conghuan Ye
Xuemin Zhang
Fang Xu
Xiaochao Zhao
机构：
[1] Hubei Engineering University,School of Computer and Information Science
来源：
Journal of Signal Processing Systems | 2021年 / 93卷
关键词：
Subspace identification; Closed-loop system; EIV (errors-in-variables) model; Instrumental variables;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper proposes a subspace identification method for closed-loop EIV (errors-in-variables) problems based on instrumental variables . First, a unified framework is derived, and then the reason is discussed why some existing subspace methods based on instrumental variables could be biased under closed-loop conditions. Afterwards a remedy is given to eliminate the bias by simply replacing the instrumental variable. Using orthogonal projection, the resulting instrumental variable method is very simple and easy to extend. In addition, simulation studies illustrate the effects of different instrumental variables.
引用
收藏
页码：345 / 355
页数：10
相关论文
共 58 条
  • [1] Chen M(2018)Spha: Smart personal health advisor based on deep analytics IEEE Communications Magazine 56 164-169
  • [2] Zhang Y(1997)Subspace algorithms for the identification of multivariable dynamic errors-in-variables models Automatica 33 1857-1869
  • [3] Qiu M(2016)Cloud infrastructure resource allocation for big data applications IEEE Transactions on Big Data 4 313-324
  • [4] Guizani N(2017)Resource management in sustainable cyber-physical systems using heterogeneous cloud computing IEEE Transactions on Sustainable Computing 3 60-72
  • [5] Hao Y(2001)Subspace identification using instrumental variable techniques Automatica 37 2005-2010
  • [6] Chou CT(2002)Subspace-based system identification: weighting and pre-filtering of instruments Automatica 38 433-443
  • [7] Verhaegen M(2005)Closed-loop subspace identification: an orthogonal projection approach Journal of Process Control 15 53-66
  • [8] Dai W(1996)A linear regression approach to state-space subspace system identification Signal Processing 52 103-129
  • [9] Qiu L(1996)Stochastic realization with exogenous inputs and ‘subspace-methods’ identification Signal Processing 52 145-160
  • [10] Wu A(2006)An overview of subspace identification Computers & Chemical Engineering 30 1502-1513
123456