DocumentCode :
3284839
Title :
State estimation for large-scale partitioned systems: a moving horizon approach
Author :
Farina, M. ; Ferrari-Trecate, Giancarlo ; Scattolini, R.
Author_Institution :
Dipt. di Elettron. e Inf., Politec. di Milano, Milan, Italy
fYear :
2010
fDate :
June 30 2010-July 2 2010
Firstpage :
3180
Lastpage :
3185
Abstract :
In this paper we propose novel state-estimation methods for large-scale discrete-time constrained linear systems that are partitioned, i.e. made by coupled subsystems with non-overlapping states. We focus on moving horizon estimation (MHE) schemes due to their capability of exploiting physical constraints on states and noise in the estimation process. We propose three different partition-based MHE (PMHE) algorithms where each subsystem solves reduced-order MHE problems to estimate its own state. Different estimators have different computational complexity, accuracy and transmission requirements among subsystems. Numerical simulations demonstrate the viability of our approach.
Keywords :
discrete time systems; large-scale systems; linear systems; reduced order systems; state estimation; computational complexity; coupled subsystem; large-scale discrete-time constrained linear system; large-scale partitioned system; moving horizon estimation; nonoverlapping state; numerical simulation; partition-based MHE algorithm; physical constraint; reduced-order MHE problem; state estimation; Computational complexity; Control systems; Couplings; Filtering; Kalman filters; Large-scale systems; Linear systems; Numerical simulation; Partitioning algorithms; State estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference (ACC), 2010
Conference_Location :
Baltimore, MD
ISSN :
0743-1619
Print_ISBN :
978-1-4244-7426-4
Type :
conf
DOI :
10.1109/ACC.2010.5530968
Filename :
5530968
Link To Document :
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