Kalman filtering for continuous descriptor systems

Author

Darouach, M. ; Boutayeb, M. ; Zasadzinski, M.

Author_Institution

CRAN-ACS-CNRS URA, Nancy I Univ., France

Volume

3

fYear

1997

fDate

4-6 Jun 1997

Firstpage

2108

Abstract

This paper deals with the problem of the state estimation for continuous stochastic descriptor systems. A new method, based on linear transformations which lead to a stochastic differential algebraic equation, is developed. Uniqueness of the estimate is achieved by choosing the optimum gain matrix which minimizes the trace of the error covariance matrix for the static subsystem, while the standard Kalman filter is derived for the dynamic subsystem. Necessary and sufficient conditions for the convergence and stability of the proposed estimator are established

Keywords

Kalman filters; continuous time systems; convergence; covariance matrices; differential equations; linear systems; state estimation; stochastic systems; Kalman filter; continuous time systems; convergence; covariance matrix; descriptor systems; gain matrix; necessary conditions; stability; state estimation; stochastic differential algebraic equation; stochastic systems; sufficient conditions; Convergence; Covariance matrix; Differential algebraic equations; Filtering; Kalman filters; Stability; State estimation; Stochastic processes; Stochastic systems; Sufficient conditions;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1997. Proceedings of the 1997

Conference_Location

Albuquerque, NM

ISSN

0743-1619

Print_ISBN

0-7803-3832-4

Type

conf

DOI

10.1109/ACC.1997.611062

Filename

611062