EconPapers    
Economics at your fingertips  
 

Distributed Observers for State Omniscience with Stochastic Communication Noises

Kairui Chen, Zhangmou Zhu, Xianxian Zeng () and Junwei Wang
Additional contact information
Kairui Chen: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Zhangmou Zhu: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Xianxian Zeng: School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510000, China
Junwei Wang: School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou 510006, China

Mathematics, 2023, vol. 11, issue 9, 1-14

Abstract: The focus of this paper is on solving the state estimation problem for general continuous-time linear systems through the use of distributed networked observers. To better reflect the communication environment, stochastic noises are considered when observers exchange information. In the networked observers, each local observer measures only part of the system output, and the state estimation can not be accomplished within a single observer. Then, all observers communicate through a pre-specified graph to make up information in the remaining system output. By solving a parametric algebraic Riccati equation (ARE), a simple method to calculate parameters in the observers is proposed. Furthermore, using the stability theory of stochastic differential equations, state omniscience is discussed in almost sure sense and in the mean square sense for the cases of state-dependent noises and non-state-dependent noises, respectively. It is shown that, for observable linear systems, the resulting observers work in a coordinated mode to reach state omniscience under the connected graph. Illustrative examples are provided to show the effectiveness of the distributed observers.

Keywords: state estimation; distributed observer; communication noises; algebraic Riccati equation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/9/1997/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/9/1997/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:9:p:1997-:d:1130875

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-03-19
Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:1997-:d:1130875