On the Use of Information Quality in Stochastic Networked Control Systems

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4 Citationer (Scopus)

Resumé

Networked control is challenged by stochastic delays that are caused by the communication networks as well as by the approach taken to exchange information about system state and set-points. Combined with stochastic changing information, there is a probability that information at the controller is not matching the true system observation, which we call mismatch probability (mmPr). The hypothesis is that the optimization of certain parameters of networked control systems targeting mmPr is equivalent to the optimization targeting control performance, while the former is practically much easier to conduct. This is first analyzed in simulation models for the example system of a wind-farm controller. As simulation analysis is subject to stochastic variability and requires large computational effort, the paper develops a Markov model of a simplified networked control system and uses numerical results from the Markov model analysis to demonstrate that mmPr based optimization can improve control performance.
OriginalsprogEngelsk
TidsskriftComputer Networks
Vol/bind124
Sider (fra-til)157-169
Antal sider12
ISSN1389-1286
DOI
StatusUdgivet - 27 jun. 2017

Citer dette

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abstract = "Networked control is challenged by stochastic delays that are caused by the communication networks as well as by the approach taken to exchange information about system state and set-points. Combined with stochastic changing information, there is a probability that information at the controller is not matching the true system observation, which we call mismatch probability (mmPr). The hypothesis is that the optimization of certain parameters of networked control systems targeting mmPr is equivalent to the optimization targeting control performance, while the former is practically much easier to conduct. This is first analyzed in simulation models for the example system of a wind-farm controller. As simulation analysis is subject to stochastic variability and requires large computational effort, the paper develops a Markov model of a simplified networked control system and uses numerical results from the Markov model analysis to demonstrate that mmPr based optimization can improve control performance.",
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On the Use of Information Quality in Stochastic Networked Control Systems. / Olsen, Rasmus Løvenstein; Madsen, Jacob Theilgaard; Rasmussen, Jakob Gulddahl; Schwefel, Hans-Peter.

I: Computer Networks, Bind 124, 27.06.2017, s. 157-169.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - On the Use of Information Quality in Stochastic Networked Control Systems

AU - Olsen, Rasmus Løvenstein

AU - Madsen, Jacob Theilgaard

AU - Rasmussen, Jakob Gulddahl

AU - Schwefel, Hans-Peter

PY - 2017/6/27

Y1 - 2017/6/27

N2 - Networked control is challenged by stochastic delays that are caused by the communication networks as well as by the approach taken to exchange information about system state and set-points. Combined with stochastic changing information, there is a probability that information at the controller is not matching the true system observation, which we call mismatch probability (mmPr). The hypothesis is that the optimization of certain parameters of networked control systems targeting mmPr is equivalent to the optimization targeting control performance, while the former is practically much easier to conduct. This is first analyzed in simulation models for the example system of a wind-farm controller. As simulation analysis is subject to stochastic variability and requires large computational effort, the paper develops a Markov model of a simplified networked control system and uses numerical results from the Markov model analysis to demonstrate that mmPr based optimization can improve control performance.

AB - Networked control is challenged by stochastic delays that are caused by the communication networks as well as by the approach taken to exchange information about system state and set-points. Combined with stochastic changing information, there is a probability that information at the controller is not matching the true system observation, which we call mismatch probability (mmPr). The hypothesis is that the optimization of certain parameters of networked control systems targeting mmPr is equivalent to the optimization targeting control performance, while the former is practically much easier to conduct. This is first analyzed in simulation models for the example system of a wind-farm controller. As simulation analysis is subject to stochastic variability and requires large computational effort, the paper develops a Markov model of a simplified networked control system and uses numerical results from the Markov model analysis to demonstrate that mmPr based optimization can improve control performance.

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DO - 10.1016/j.comnet.2017.06.006

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VL - 124

SP - 157

EP - 169

JO - Computer Networks

JF - Computer Networks

SN - 1389-1286

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