Robust receding-horizon state estimation for uncertain discrete-time linear systems

被引：43
|
作者：
Alessandri, A
Baglietto, M
Battistelli, G
机构：
[1] CNR, CNR, ISSIA, I-16149 Genoa, Italy
[2] Univ Genoa, Dept Commun Comp & Syst Sci, DIST, I-16145 Genoa, Italy
来源：
SYSTEMS & CONTROL LETTERS | 2005年 / 54卷 / 07期
关键词：
state estimation; receding horizon; uncertain linear systems; robustness; minimax optimization;
D O I：
10.1016/j.sysconle.2004.11.003
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An approach to robust receding-horizon state estimation for discrete-time linear systems is presented. Estimates of the state variables can be obtained by minimizing a worst-case quadratic cost function according to a sliding-window strategy. This leads to state the estimation problem in the form of a regularized least-squares one with uncertain data. The optimal solution (involving on-line scalar minimization) together with a suitable closed-form approximation are given. The stability properties of the estimation error for both the optimal filter and the approximate one have been studied and conditions to select the design parameters are proposed. Simulation results are reported to show the effectiveness of the proposed approach. (c) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码：627 / 643
页数：17
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