Functional Adaptive Control for Nonlinear Stochastic Systems in Presence of Outliers

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F09%3A43898473" target="_blank" >RIV/49777513:23520/09:43898473 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3182/20090706-3-FR-2004.00251" target="_blank" >http://dx.doi.org/10.3182/20090706-3-FR-2004.00251</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3182/20090706-3-FR-2004.00251" target="_blank" >10.3182/20090706-3-FR-2004.00251</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Functional Adaptive Control for Nonlinear Stochastic Systems in Presence of Outliers

Popis výsledku v původním jazyce

This paper presents an enhancement of a functional adaptive control of nonlinear stochastic systems that renders it to be robust with respect to the occurrence of outliers in the plant measured output. Outliers are considered to be large deviations of asignal being measured, only occurring in a few percent of the observations. Therefore, although rare, the outliers cause poor parameter estimates and, consequently, heavily degrade control performance due to their large amplitude. A system is modelled using a multi-layer perceptron network and the measurement noise is modelled by a mixture of Gaussian distributions. One component of the mixture describes uncorrupted process data, while the others describe various types of outliers. Parameters of the network together with output prediction of the uncorrupted data component are estimated by an estimation method based on the mixture of Gaussian distributions. Control design is based on a bicriterial dual approach. The advantages of the pro

Název v anglickém jazyce

Functional Adaptive Control for Nonlinear Stochastic Systems in Presence of Outliers

Popis výsledku anglicky

This paper presents an enhancement of a functional adaptive control of nonlinear stochastic systems that renders it to be robust with respect to the occurrence of outliers in the plant measured output. Outliers are considered to be large deviations of asignal being measured, only occurring in a few percent of the observations. Therefore, although rare, the outliers cause poor parameter estimates and, consequently, heavily degrade control performance due to their large amplitude. A system is modelled using a multi-layer perceptron network and the measurement noise is modelled by a mixture of Gaussian distributions. One component of the mixture describes uncorrupted process data, while the others describe various types of outliers. Parameters of the network together with output prediction of the uncorrupted data component are estimated by an estimation method based on the mixture of Gaussian distributions. Control design is based on a bicriterial dual approach. The advantages of the pro

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název periodika

IFAC-PapersOnline

ISSN

1474-6670

e-ISSN

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Svazek periodika

15

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

FR - Francouzská republika

Počet stran výsledku

6

Strana od-do

1505-1510

Kód UT WoS článku

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EID výsledku v databázi Scopus

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