.png){kind=icon}
Return
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}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
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.
Keywords:
dual linear quadratic regulator (LQR)
Lyapunov equation
modified Riccati equation
static output feedback
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
Organization
No organization information available
Cited Papers
ShareSaveTemporal 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
OAAIShareSaveShareSaveShareSave no more
no more