Input Design for Fault Detection Using Extended Kalman Filter and Reinforcement Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43932676" target="_blank" >RIV/49777513:23520/17:43932676 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.ifacol.2017.08.1461" target="_blank" >http://dx.doi.org/10.1016/j.ifacol.2017.08.1461</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ifacol.2017.08.1461" target="_blank" >10.1016/j.ifacol.2017.08.1461</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Input Design for Fault Detection Using Extended Kalman Filter and Reinforcement Learning

Popis výsledku v původním jazyce

The paper deals with an active fault detection problem for jump Markov nonlinear systems with Gaussian noises. The problem is formulated as a functional optimization problem over an infinite-time horizon with a general discounted detection criterion. The design of an active fault detector is performed in two steps. First, the original problem is recast as a perfect state information problem by complementing the nonlinear system with a suboptimal state estimator based on a bank of extended Kalman Filters. Then, a temporal-difference learning algorithm is used to train the active fault detector such that the criterion is minimized. A simulation example of a differential wheeled robot is used to illustrate the performance of the proposed design.

Název v anglickém jazyce

Input Design for Fault Detection Using Extended Kalman Filter and Reinforcement Learning

Popis výsledku anglicky

The paper deals with an active fault detection problem for jump Markov nonlinear systems with Gaussian noises. The problem is formulated as a functional optimization problem over an infinite-time horizon with a general discounted detection criterion. The design of an active fault detector is performed in two steps. First, the original problem is recast as a perfect state information problem by complementing the nonlinear system with a suboptimal state estimator based on a bank of extended Kalman Filters. Then, a temporal-difference learning algorithm is used to train the active fault detector such that the criterion is minimized. A simulation example of a differential wheeled robot is used to illustrate the performance of the proposed design.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

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

2017

Kód důvěrnosti údajů

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

Název periodika

IFAC-PapersOnLine

ISSN

2405-8963

e-ISSN

—

Svazek periodika

50

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

6

Strana od-do

7302-7307

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85031783964