Predictive Dual Control for Nonlinear Stochastic Systems Modelled by Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F11%3A43898212" target="_blank" >RIV/49777513:23520/11:43898212 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/MED.2011.5983106" target="_blank" >http://dx.doi.org/10.1109/MED.2011.5983106</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/MED.2011.5983106" target="_blank" >10.1109/MED.2011.5983106</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predictive Dual Control for Nonlinear Stochastic Systems Modelled by Neural Networks

Popis výsledku v původním jazyce

A predictive dual control for a nonlinear system with functional uncertainty based on the bicriterial approach is proposed and discussed. The nonlinear functions of the system are approximated by multi-layered perceptron neural networks where the unknownparameters are found in real time without a necessity of any off-line training process. These nonlinear predictors based on the af?ne structure in inputs together with the certainty equivalence principle utilization allow to obtain an analytical solution to the predictive control. Behavior of the system based on the certainty equivalence assumption can negatively be affected, especially in a presence of disturbances and functional uncertainties. For that, the obtained predictive control is enhanced about dual property based on the bicriterial approach that uses two separate criteria to introduce one of the opposing aspects between estimation and control. The quality of the proposed predictive dual controller is illustrated in a numeric

Název v anglickém jazyce

Predictive Dual Control for Nonlinear Stochastic Systems Modelled by Neural Networks

Popis výsledku anglicky

A predictive dual control for a nonlinear system with functional uncertainty based on the bicriterial approach is proposed and discussed. The nonlinear functions of the system are approximated by multi-layered perceptron neural networks where the unknownparameters are found in real time without a necessity of any off-line training process. These nonlinear predictors based on the af?ne structure in inputs together with the certainty equivalence principle utilization allow to obtain an analytical solution to the predictive control. Behavior of the system based on the certainty equivalence assumption can negatively be affected, especially in a presence of disturbances and functional uncertainties. For that, the obtained predictive control is enhanced about dual property based on the bicriterial approach that uses two separate criteria to introduce one of the opposing aspects between estimation and control. The quality of the proposed predictive dual controller is illustrated in a numeric

Druh

D - Stať ve sborníku

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)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceeding of the 19th Mediterranean Conference on Control and Automation

ISBN

978-1-4577-0125-2

ISSN

—

e-ISSN

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Počet stran výsledku

6

Strana od-do

1277-1282

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Corfu, Greece

Datum konání akce

20. 6. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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