Constrained state estimation for nonlinear discrete-time systems: Stability and moving horizon approximations

被引：629

作者：

Rao, CV ^{[1
]}

Rawlings, JB

Mayne, DQ

机构：

[1] Univ Calif Berkeley, Dept Bioengn, Berkeley, CA 94720 USA

[2] Univ Wisconsin, Dept Chem Engn, Madison, WI 53706 USA

[3] Univ London Imperial Coll Sci Technol & Med, Dept Elect & Elect Engn, London SW7 2BT, England

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2003年 / 48卷 / 02期

基金：

美国国家科学基金会;

关键词：

constraints; model predictive control (MPC); moving horizon estimation (MHE); optimization; state estimation;

D O I：

10.1109/TAC.2002.808470

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

State estimator design for a nonlinear discrete-time system is a challenging problem, further complicated when additional physical insight is available in the form of inequality constraints on the slate variables and disturbances. One strategy for constrained state estimation is to employ online optimization using a moving horizon approximation. In this article we propose a general theory for constrained moving horizon estimation. Sufficient conditions for asymptotic and bounded stability are established. We apply these results to develop a practical algorithm for constrained linear and nonlinear state estimation. Examples are used to illustrate the benefits of constrained state estimation. Our framework is deterministic.

引用

页码：246 / 258

页数：13

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