Open AccessEstimation of network traffic status and switching control of networked control systems with data dropout
Author(s) -
Zanma Tadanao,
Hashimoto Daiki,
Koiwa Kenta,
Liu KangZhi
Publication year - 2022
Publication title -
iet cyber‐physical systems: theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 7
ISSN - 2398-3396
DOI - 10.1049/cps2.12024
Subject(s) - dropout (neural networks) , markov chain , computer science , network traffic control , traffic generation model , controller (irrigation) , network congestion , markov chain monte carlo , computer network , real time computing , network packet , machine learning , artificial intelligence , bayesian probability , agronomy , biology
The recent development of the communication technology accelerates studies of real‐time networked control systems using networks. The data dropout is essentially unavoidable, especially in wireless networks and it results from transmission errors and network traffic congestion. Multiple time‐varying network traffic status given by discrete‐time homogeneous Markov chains is assumed. The authors estimate the network traffic status characterised by the probability matrix of the Markov chain online from the data dropout history. According to the estimation of network traffic status, an appropriate controller is selected to improve the control performance. The effectiveness of the proposed method is verified through simulations and experiments.