MIN-MAX MOVING-HORIZON ESTIMATION FOR UNCERTAIN DISCRETE-TIME LINEAR SYSTEMS

被引：34
作者：
Alessandri, A. [1 ]
Baglietto, M. [2 ]
Battistelli, G. [3 ]
机构：
[1] Univ Genoa, Dept Mech Engn, I-16129 Genoa, Italy
[2] Univ Genoa, Dept Commun Comp & Syst Sci, I-16145 Genoa, Italy
[3] Univ Florence, Dipartimento Sistemi & Informat, I-50139 Florence, Italy
来源：
SIAM JOURNAL ON CONTROL AND OPTIMIZATION | 2012年 / 50卷 / 03期
关键词：
moving-horizon state estimation; min-max least squares; stability; semidefinite programming; Lagrangian relaxation; STATE ESTIMATION; OPTIMIZATION; STABILITY; OBSERVERS; MODELS;
D O I：
10.1137/090762798
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Moving-horizon state estimation is addressed for a class of uncertain discrete-time linear systems with disturbances acting on the dynamic and measurement equations. The estimates are obtained by minimizing a least-squares cost function in the worst case. The resulting min-max problem can be solved by using semidefinite programming or Lagrangian relaxation, which allows one to determine approximate estimates with a reduced computational burden. Assuming a certain error in the solution of such min-max optimization problems, the stability of the estimation error in the presence of bounded disturbances is guaranteed under suitable conditions. Explicit bounding sequences on the estimation are derived. Simulation results are reported showing benefits of the proposed approach in terms of computational tractability and performances as compared with alternative methodologies.
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收藏
页码：1439 / 1465
页数：27
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