Semidefinite programming solutions to robust state estimation problem with model uncertainties

T. Ratnarajah, Z. Q. Luo, K. M. Wong

Research output: Contribution to journalConference articlepeer-review

Abstract

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, which accommodate all conceivable uncertainties and therefore lead to overly conservative solutions.

Original languageEnglish (US)
Pages (from-to)275-276
Number of pages2
JournalProceedings of the IEEE Conference on Decision and Control
Volume1
StatePublished - Dec 1 1998
EventProceedings of the 1998 37th IEEE Conference on Decision and Control (CDC) - Tampa, FL, USA
Duration: Dec 16 1998Dec 18 1998

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