.png){kind=icon}
返回
Subspace identification with eigenvalue constraints
.png){kind=icon}
PRE
AI
DOI：10.1016/j.automatica.2013.04.028.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Standard subspace methods for the identification of discrete-time, linear, time-invariant systems are transformed into generalized convex optimization problems in which the poles of the system estimate are constrained to lie within user-defined convex regions of the complex plane. The transformation is done by restating subspace methods such as the minimization of a Frobenius norm affine in the estimate parameters, allowing the minimization to be augmented with convex constraints. The constraints are created using linear-matrix-inequality regions, which generalize standard Lyapunov stability to arbitrary convex regions of the complex plane. The algorithm is developed for subspace methods based on estimates of the extended observability matrix and methods based on estimates of state sequences, but it is extendable to all subspace methods. Simulation examples demonstrate the utility of the proposed method. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
System identification
Subspace identification
Linear-matrix inequalities
Linear systems
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.9
论文数:
1.2W
被引数:
5.2W
机构
引用论文
.png){kind=icon}
Co-Designing for Equity in Informal Science Learning: A Proof-of-Concept Study of Design Principles
OAAI
OAAI分享收藏 没有更多内容
没有更多内容