Distributed state estimation for cyber-physical systems under Round-Robin communication protocol

被引：0

作者：

Zhu F.-Z. ^{[2
]}

Peng L. ^{[1
,3
]}

机构：

[1] Engineering Research Center for Internet of Things Technology Application Ministry of Education, Jiangnan University, Jiangsu, Wuxi

[2] School of Automation and Electrical Engineering, Linyi University, Shandong, Linyi

[3] Key Laboratory of Internet of Things Application Technology, Wuxi Taihu University, Jiangsu, Wuxi

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2022年 / 39卷 / 10期

基金：

中国国家自然科学基金;

关键词：

distributed filtering; Round-Robin protocol; switching topology; wireless sensor network;

D O I：

10.7641/CTA.2022.10628

中图分类号：

学科分类号：

摘要：

This paper is concerned with the problem of distributed state estimation for a class of cyber-physical systems. Since sensor networks have limited communication bandwidth, data conflicts may occur when a large number of nodes send data at the same time. The Round-Robin protocol is introduced to ease the communication burden of the sensor network, where the measurement components of each node access the network sequentially and periodically. Considering that the topology switching probability matrices of the filtering network are time-varying, a non-homogeneous Markov chain is used to describe the stochastic topology switching behavior. It is shown that the estimation errors converge in an exponentially decaying form, which ensures that the estimation error system is eventually bounded in the mean square sense. Furthermore, the desired distributed filter parameters can be obtained by solving a specific topology-dependent convex optimization problem. Finally, the feasibility of the designed distributed state estimation method is demonstrated by two examples. © 2022 South China University of Technology. All rights reserved.

引用

页码：1925 / 1936

页数：11

共 29 条

[1] JABBAR M A, SAMREEN S, ALUVALU R, Et al., Cyber physical systems for smart cities development, International Journal of Engineering and Technology, 7, 4, pp. 36-38, (2018)
[2] SAMPIGETHAYA K, POOVENDRAN R., Aviation cyber-physical systems: Foundations for future aircraft and air transport, Proceedings of the IEEE, 101, 8, pp. 1834-1855, (2013)
[3] ZHU F, PENG L., Distributed adaptive event-triggered iterative learning controller based on a filtering network, Transactions of the Institute of Measurement and Control, 43, 14, pp. 3220-3232, (2021)
[4] LIN C Y, ZEADALLY S, CHEN T S, Et al., Enabling cyber physical systems with wireless sensor networking technologies, International Journal of Distributed Sensor Networks, 8, 5, (2012)
[5] WU F J, KAO Y F, TSENG Y C., From wireless sensor networks towards cyber physical systems, Pervasive and Mobile Computing, 7, 4, pp. 397-413, (2011)
[6] AHMADI A, MORADI M, CHERIFI C, Et al., Wireless connectivity of cps for smart manufacturing: A survey, Proceedings of International Conference on Software, Knowledge, Information Management and Applications, pp. 1-8, (2018)
[7] ZHU F, LIU X, PENG L., Adaptive consensus-based distributed H∞ filtering with switching topology subject to partial information on transition probabilities, Applied Mathematics and Computation, 411, (2021)
[8] DING D, TANG Z, WANG Y, Et al., Secure synchronization of complex networks under deception attacks against vulnerable nodes, Applied Mathematics and Computation, 399, (2021)
[9] XIE Kexin, YANG Chunxi, LIU Hua, Et al., Packet-dropout performance and energy optimization of the distributed Kalman consensus filter, Control Theory & Applications, 35, 8, pp. 1177-1185, (2018)
[10] CHEN Junyong, WU Yilin, QI Tian, Consensus-based distributed filtering algorithm in sensor networks, Control Theory & Applications, 28, 12, pp. 1729-1739, (2011)

← 1 2 3 →