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Wyszukujesz frazę "Rohal’-Ilkiv, B" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Application of adaptive multivariable Generalized Predictive Control to a HVAC system in real time
Autorzy:
Gulan, M.
Salaj, M.
Rohal’-Ilkiv, B
Powiązania:
https://bibliotekanauki.pl/articles/229355.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
predictive control
multivariable systems
constrained optimization
HVAC system
cascade control
Opis:
This paper presents the application of a Multivariable Generalized Predictive Controller (MGPC) for simultaneous temperature and humidity control in a Heating, Ventilating and Air- Conditioning (HVAC) system. The multivariable controlled process dynamics is modeled using a set of MISO models on-line identified from measured input-output process data. The controller synthesis is based on direct optimization of selected quadratic cost function with respect to amplitude and rate input constraints. Efficacy of the proposed adaptive MGPC algorithm is experimentally demonstrated on a laboratory-scale model of HVAC system. To control the airconditioning part of system the designed multivariable predictive controller is considered in a cascade dual-rate control scheme with PID auxiliary controllers.
Źródło:
Archives of Control Sciences; 2014, 24, 1; 67-84
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Real-time implementation of multiple model based predictive control strategy to air/fuel ratio of a gasoline engine
Autorzy:
Wojnar, S
Polóni, T
Šimončič, P
Rohal’-Ilkiv, B
Honek, M
Csambál, J
Powiązania:
https://bibliotekanauki.pl/articles/229632.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
model predictive control
multiple models
air/fuel ratio
spark ignition engine
ARX models
Opis:
Growing safety, pollution and comfort requirements influence automotive industry ever more. The use of three-way catalysts in exhaust aftertreatment systems of combustion engines is essential in reducing engine emissions to levels demanded by environmental legislation. However, the key to the optimal catalytic conversion level is to keep the engine air/fuel ratio (AFR) at a desired level. Thus, for this purposes more and more sophisticated AFR control algorithms are intensively investigated and tested in the literature. The goal of this paper is to present for a case of a gasoline engine the model predictive AFR controller based on the multiple-model approach to the engine modeling. The idea is to identify the engine in particular working points and then to create a global engine's model using Sugeno fuzzy logic. Opposite to traditional control approaches which lose their quality beside steady state, it enables to work with satisfactory quality mainly in transient regimes. Presented results of the multiple-model predictive air/fuel ratio control are acquired from the first experimental real-time implementation on the VW Polo 1390 cm3 gasoline engine, at which the original electronic control unit (ECU) has been fully replaced by a dSpace prototyping system which execute the predictive controller. Required control performance has been proven and is presented in the paper.
Źródło:
Archives of Control Sciences; 2013, 23, 1; 93-106
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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