Model-free distributed state estimation with local measurements.

IF 2.7 2区 数学 Q1 MATHEMATICS, APPLIED Chaos Pub Date : 2024-11-01 DOI:10.1063/5.0235313
Kepan Gao, Chenyu Ran, Xiaoling Wang, Liu Liu, Guo-Ping Jiang
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Abstract

In this paper, the state estimation problem of physical plants with unknown system dynamic is revisited from the perspective of limited output information measurement, which corresponds to those with characteristics of high-dimensional, wide-area coverage and scatter. Given this fact, a network of sensors are used to carry out the measurement with each one accessing only partial outputs of the targeted systems and a novel model-free state estimation approach, named distributed stochastic variational inference state estimation, is proposed. The key idea of this method is to compensate for the impacts of local output measurements by adding nearest-neighbor rule-based information interaction among estimators to complete the state estimation. It finds from the numerical experiments that the proposed method has clear advantages in both estimation accuracy and speed, and it also provides guidance on how to improve the efficiency of state estimation under local measurements.

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利用局部测量进行无模型分布式状态估计。
本文从有限输出信息测量的角度重新审视了具有未知系统动态的物理工厂的状态估计问题,这与那些具有高维、广域覆盖和散射特性的系统相对应。考虑到这一事实,我们使用传感器网络进行测量,每个传感器只获取目标系统的部分输出,并提出了一种新型的无模型状态估计方法,即分布式随机变分推理状态估计。该方法的主要思想是通过在估计器之间增加基于近邻规则的信息交互来补偿局部输出测量的影响,从而完成状态估计。通过数值实验发现,所提出的方法在估计精度和速度上都有明显的优势,同时也为如何提高局部测量下的状态估计效率提供了指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Chaos
Chaos 物理-物理:数学物理
CiteScore
5.20
自引率
13.80%
发文量
448
审稿时长
2.3 months
期刊介绍: Chaos: An Interdisciplinary Journal of Nonlinear Science is a peer-reviewed journal devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.
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