Resumé
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Computer Networks |
| Vol/bind | 124 |
| Sider (fra-til) | 157-169 |
| Antal sider | 12 |
| ISSN | 1389-1286 |
| DOI | |
| Status | Udgivet - 27 jun. 2017 |
Citer dette
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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 tidsskrift › Tidsskriftartikel › Forskning › peer 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.
U2 - 10.1016/j.comnet.2017.06.006
DO - 10.1016/j.comnet.2017.06.006
M3 - Journal article
VL - 124
SP - 157
EP - 169
JO - Computer Networks
JF - Computer Networks
SN - 1389-1286
ER -