Observability Gramian for Bayesian Inference in Nonlinear Systems With Its Industrial Application

被引：0
|
作者：
Kunwoo, Lee [1 ]
Umezu, Yusuke [2 ]
Konno, Kaiki [2 ]
Kashima, Kenji [1 ]
机构：
[1] Kyoto Univ, Grad Sch Informat, Kyoto 6068501, Japan
[2] Kawasaki Heavy Ind Co Ltd, Corp Technol Div, Akashi, Hyogo 6738666, Japan
来源：
IEEE CONTROL SYSTEMS LETTERS | 2023年 / 7卷
关键词：
Bayesian Fisher information; Bayesian state estimation; data-driven oveservability analysis; non-linear systems; observability Gramian; MODEL-REDUCTION;
D O I：
10.1109/LCSYS.2022.3227452
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this letter, we present a novel (empirical) observability Gramian for nonlinear stochastic systems in the light of Bayesian inference. First, we define our observability Gramian, which we refer to as the estimability Gramian, based on the relation to the so-called Bayesian Fisher Information Matrix for initial state estimation. Then, we study the fundamental properties of an empirical version of the estimability Gramian. The practical usefulness of the proposed framework is examined through its application to a parameter and initial state estimation in a natural gas engine cylinder.
引用
收藏
页码：871 / 876
页数：6
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