Semidefinite programming solutions to robust state estimation problem with model uncertainties

被引：0

作者：

Ratnarajah, T ^{[1
]}

Luo, ZQ ^{[1
]}

Wong, KM ^{[1
]}

机构：

[1] McMaster Univ, Commun Res Lab, Hamilton, ON L8S 4K1, Canada

来源：

PROCEEDINGS OF THE 37TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-4 | 1998年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a novel finite-horizon, discrete-time, time-varying state estimation method is proposed based on the recent robust semidefinite programming (RSDP) technique. The proposed formulation guarantees a robust performance with respect to model uncertainties which are known to lie within certain a priori bounds. This is in contrast to earlier robust designs, such as H-infinity, which accommodate all conceivable uncertainties and therefore lead to overly conservative solutions.

引用

页码：275 / 276

页数：2

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