.png){kind=icon}
返回
A convergent algorithm for computing stabilizing static output feedback gains
.png){kind=icon}
PRE
AI
DOI：10.1109/TAC.2004.838482.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We revisit the approach by Cao et al. that uses a fixed-structure control law to find stabilizing static output feedback gains for linear time-invariant systems. By performing singular value decomposition on the output matrix, together with similarity transformations, we present a new stabilization method. Unlike their results that involve a difficult modified Riccati equation whose solution is coupled with other two intermediate matrices that are difficult to find, we obtain Lyapunov equations. We present a convergent algorithm to solve the new design equations for the gains. We will show that our new approach, like theirs, is a dual optimal output feedback linear quadratic regulator theory. Numerical examples are given to illustrate the effectiveness of the algorithm and validate the new method.
Keyword:
dual linear quadratic regulator (LQR)
Lyapunov equation
modified Riccati equation
static output feedback
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:
7
论文数:
1.3W
被引数:
6.7W
机构
暂无机构信息
引用论文
Temporal Patterns in Reproductive Success in the Endangered Roseate Tern (Sterna dougallii) Nesting on Long Island, New York, and Bird Island, Massachusetts.png)
The Auk
OAAI
The AukIF0
OAAI分享收藏分享收藏分享收藏 没有更多内容
没有更多内容