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
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;
Conference_Titel :
American Control Conference, 1999. Proceedings of the 1999
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-4990-3
DOI :
10.1109/ACC.1999.782393
