.png){kind=icon}
返回
Minimum variance constrained estimator
.png){kind=icon}
OA
AI
DOI：10.1016/j.automatica.2021.110106.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper is concerned with the problem of state estimation for discrete-time linear systems in the presence of additional (equality or inequality) constraints on the state (or estimate). By use of the minimum variance duality, the estimation problem is converted into an optimal control problem. Two algorithmic solutions are described: the full information estimator (FIE) and the moving horizon estimator (MHE). The main result is to show that the proposed estimator is stable in the sense of an observer. The proposed algorithm is distinct from the standard algorithm for constrained state estimation based upon the use of the minimum energy duality. The two are compared numerically on the benchmark batch reactor process model. (C) 2021 Elsevier Ltd. All rights reserved.
Keyword:
Constrained estimation
MHE
Kalman filter
Minimum variance duality
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.9
论文数:
1.2W
被引数:
5.2W
机构
引用论文
A new approach to constrained state estimation for discrete-time linear systems with unknown inputsOAAI
OAAI分享收藏CasADi: a software framework for nonlinear optimization and optimal controlCasADi: 非线性优化和最优控制的软件框架
PREAI
分享收藏Extension of minimum variance estimation for systems with unknown inputs未知输入系统最小方差估计的推广.png)
AUTOMATICAOAAI
AUTOMATICAIF5.9
OAAI分享收藏Stochastic predictive control under intermittent observations and unreliable actionsAUTOMATICAOAAI
AUTOMATICAIF5.9
OAAI分享收藏分享收藏
查看更多内容