Distributed state estimation and fault diagnosis using reduced sensitivity to neighbor estimates with application to building control

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21720%2F23%3A00363506" target="_blank" >RIV/68407700:21720/23:00363506 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/23:00363506

Výsledek na webu

<a href="https://doi.org/10.1016/j.jfranklin.2022.10.017" target="_blank" >https://doi.org/10.1016/j.jfranklin.2022.10.017</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jfranklin.2022.10.017" target="_blank" >10.1016/j.jfranklin.2022.10.017</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Distributed state estimation and fault diagnosis using reduced sensitivity to neighbor estimates with application to building control

Popis výsledku v původním jazyce

This paper proposes solutions that reduce the inaccuracy of distributed state estimation and consequent performance deterioration of distributed model predictive control caused by faults and inaccurate models. A distributed state estimation method for large-scale systems is introduced. A local state estimation approach considers the uncertainty of neighbor estimates, which can improve the state estimation accuracy, whereas it keeps a low network communication burden. The method also incorporates the uncertainty of model parameters which improves the performance when using simplified models. The proposed method is extended with multiple models and estimates the probability of nominal and fault behavior models, which creates a distributed fault detection and diagnosis method. An example with application to the building heating control demonstrates that the proposed algorithm provides accurate state estimates to a controller and detects local or global faults while using simplified models.

Název v anglickém jazyce

Distributed state estimation and fault diagnosis using reduced sensitivity to neighbor estimates with application to building control

Popis výsledku anglicky

This paper proposes solutions that reduce the inaccuracy of distributed state estimation and consequent performance deterioration of distributed model predictive control caused by faults and inaccurate models. A distributed state estimation method for large-scale systems is introduced. A local state estimation approach considers the uncertainty of neighbor estimates, which can improve the state estimation accuracy, whereas it keeps a low network communication burden. The method also incorporates the uncertainty of model parameters which improves the performance when using simplified models. The proposed method is extended with multiple models and estimates the probability of nominal and fault behavior models, which creates a distributed fault detection and diagnosis method. An example with application to the building heating control demonstrates that the proposed algorithm provides accurate state estimates to a controller and detects local or global faults while using simplified models.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/TK01020024" target="_blank" >TK01020024: Hydronics 4.0</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of the Franklin Institute

ISSN

0016-0032

e-ISSN

1879-2693

Svazek periodika

360

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

24

Strana od-do

9216-9239

Kód UT WoS článku

001047055500001

EID výsledku v databázi Scopus

2-s2.0-85143546044