Optimal Active Fault Diagnosis by Temporal-Difference Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F16%3A43929733" target="_blank" >RIV/49777513:23520/16:43929733 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/CDC.2016.7798581" target="_blank" >http://dx.doi.org/10.1109/CDC.2016.7798581</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CDC.2016.7798581" target="_blank" >10.1109/CDC.2016.7798581</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimal Active Fault Diagnosis by Temporal-Difference Learning

Popis výsledku v původním jazyce

In this paper, a novel solution to the active fault diagnosis problem for stochastic linear Markovian switching systems on the infinite-time horizon is proposed. The imperfect state information problem of designing an active fault detector that minimizes a general detection cost criterion is reformulated as the perfect state information problem using sufficient statistics. The reformulation decreases theoretical complexity and enables to find a suboptimal solution by dynamic programming. However, classical approaches are computationally complex or fail to identify the most representative states of the system. This paper combines the active fault detection, state estimation, and reinforcement learning. In the proposed algorithm, temporal difference learning is used to train the active fault detector based on input-output data from the system simulation. The designed detector can be then used online. A numerical example is presented to verify the proposed algorithm.

Název v anglickém jazyce

Optimal Active Fault Diagnosis by Temporal-Difference Learning

Popis výsledku anglicky

In this paper, a novel solution to the active fault diagnosis problem for stochastic linear Markovian switching systems on the infinite-time horizon is proposed. The imperfect state information problem of designing an active fault detector that minimizes a general detection cost criterion is reformulated as the perfect state information problem using sufficient statistics. The reformulation decreases theoretical complexity and enables to find a suboptimal solution by dynamic programming. However, classical approaches are computationally complex or fail to identify the most representative states of the system. This paper combines the active fault detection, state estimation, and reinforcement learning. In the proposed algorithm, temporal difference learning is used to train the active fault detector based on input-output data from the system simulation. The designed detector can be then used online. A numerical example is presented to verify the proposed algorithm.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/LO1506" target="_blank" >LO1506: Podpora udržitelnosti centra NTIS - Nové technologie pro informační společnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 55th IEEE Conference on Decision and Control (CDC)

ISBN

978-1-5090-1837-6

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

2146-2151

Název nakladatele

IEEE

Místo vydání

neuveden

Místo konání akce

Las Vegas, USA

Datum konání akce

12. 12. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000400048102053