DocumentCode
3033397
Title
Stability of constrained linear moving horizon estimation
Author
Rao, Christopher V. ; Rawlings, James B. ; Lee, Jay H.
Author_Institution
Dept. of Chem. Eng., Wisconsin Univ., Madison, WI, USA
Volume
5
fYear
1999
fDate
1999
Firstpage
3387
Abstract
We derive sufficient conditions for the stability of moving horizon state estimation with linear models subject to constraints on the estimate. The key result is that if the time-varying or steady-state Kalman filter covariance update is used to summarize the prior data, then the estimator is stable in the sense of an observer, even in the presence of constraints
Keywords
Kalman filters; discrete time systems; dynamic programming; linear systems; observers; stability; Kalman filter; discrete time systems; dynamic programming; linear models; linear time invariant systems; moving horizon estimation; observer; stability; state estimation; sufficient conditions; Chemical engineering; Linear systems; Measurement standards; Observers; Random variables; Stability; State estimation; Steady-state; Sufficient conditions; Time varying systems;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 1999. Proceedings of the 1999
Conference_Location
San Diego, CA
ISSN
0743-1619
Print_ISBN
0-7803-4990-3
Type
conf
DOI
10.1109/ACC.1999.782393
Filename
782393
Link To Document