Numerical robustness of extended Kalman filtering based state estimation in ill‐conditioned continuous‐discrete nonlinear stochastic chemical systems

平方根 稳健性(进化) 卡尔曼滤波器 控制理论(社会学) 估计员 非线性系统 均方根 数学 力矩(物理) 应用数学 计算机科学 随机微分方程 工程类 统计 人工智能 几何学 化学 控制(管理) 物理 电气工程 基因 经典力学 量子力学 生物化学
作者
Gennady Yu. Kulikov,Maria V. Kulikova
出处
期刊:International Journal of Robust and Nonlinear Control [Wiley]
卷期号:29 (5): 1377-1395 被引量:35
标识
DOI:10.1002/rnc.4440
摘要

Summary This paper presents a case study investigation of numerical robustness of extended Kalman filters used for estimation of stochastic chemical systems with ill‐conditioned measurements. Here, we consider both a batch reactor model and that of a continuously stirred tank reactor. Our purpose is to explore performance of extended Kalman filtering–based state estimators when the measurement model becomes increasingly ill conditioned. In this way, we determine numerically robust methods, which are suitable for accurate estimation of stochastic chemical systems in the presence of round‐off and other disturbances. We examine both conventional filters and their square‐root forms. All these algorithms are implemented by means of the conventional matrix inversion used in their measurement update steps and the Moore‐Penrose pseudoinversion as well. Furthermore, the square‐root filters under investigation are obtained in two ways, namely, by solving square‐root moment differential equations and by square rooting the filter itself. We show that only the square‐root filters grounded in the second approach (with use of stable orthogonal decompositions) are numerically robust and provide the excellent estimation accuracy within all our ill‐conditioned stochastic chemical system scenarios considered in this paper. In addition, the convectional filters and the square‐root variants based on solving moment equations are rather sensitive to round‐off and may be useful and accurate if the chemical system at hand is rather well conditioned.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
在水一方应助lizibelle采纳,获得10
1秒前
hoyden完成签到,获得积分10
1秒前
Stardust发布了新的文献求助10
2秒前
4秒前
momo发布了新的文献求助10
5秒前
5秒前
乐乐应助Mo采纳,获得10
6秒前
6秒前
Liufgui应助Z6kjoA采纳,获得20
6秒前
爆米花应助科研通管家采纳,获得10
8秒前
NexusExplorer应助科研通管家采纳,获得10
8秒前
cangy发布了新的文献求助10
8秒前
英姑应助科研通管家采纳,获得10
8秒前
地表飞猪应助科研通管家采纳,获得10
8秒前
桐桐应助科研通管家采纳,获得10
8秒前
9秒前
Akim应助科研通管家采纳,获得10
9秒前
Orange应助科研通管家采纳,获得10
9秒前
地表飞猪应助科研通管家采纳,获得10
9秒前
科研通AI2S应助科研通管家采纳,获得10
9秒前
酷波er应助科研通管家采纳,获得10
9秒前
地表飞猪应助科研通管家采纳,获得10
9秒前
9秒前
YamDaamCaa应助科研通管家采纳,获得30
9秒前
luo关闭了luo文献求助
10秒前
Jogging完成签到,获得积分10
11秒前
Villanellel发布了新的文献求助30
11秒前
李健应助ttt采纳,获得10
11秒前
科目三应助xueyu采纳,获得10
12秒前
Jello完成签到,获得积分10
15秒前
15秒前
111完成签到,获得积分10
16秒前
qq完成签到 ,获得积分10
16秒前
17秒前
hhhblabla应助ZZZ采纳,获得20
17秒前
17秒前
打打应助雨的痕迹采纳,获得10
19秒前
19秒前
好好发布了新的文献求助10
20秒前
西海岸的风完成签到 ,获得积分10
20秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989334
求助须知:如何正确求助?哪些是违规求助? 3531428
关于积分的说明 11253936
捐赠科研通 3270119
什么是DOI,文献DOI怎么找? 1804887
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809173