Generalized Predictive Control with a Non-linear Autoregressive Model

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F05%3APU50363" target="_blank" >RIV/00216305:26220/05:PU50363 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Generalized Predictive Control with a Non-linear Autoregressive Model

Original language description

This paper presents a solution to computation of predictive control using non-linear auto-regressive models. For the non-linear model a neural network is used as a perspective tool for modelling of dynamic systems. However, the described approach is applicable to any type of auto-regressive model. The model is not linearized in the operating point, but in each control optimization step the model's derivative is computed (linearization) for all points in the prediction horizon. The method can be usedd inreal-time control. This is verified by porting the algorithm directly to the PLC.

Czech name

Prediktivní řízení s nelineárním autoregresivním modelem.

Czech description

Článek se zabývá možnostmi použití neuronových sítí v prediktivních řídicích algoritmech.

Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

BC - Theory and management systems

OECD FORD branch

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Project

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Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2005

Confidentiality

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

Name of the periodical

WSEAS Transactions on Circuits

ISSN

1109-2734

e-ISSN

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Volume of the periodical

2005

Issue of the periodical within the volume

3

Country of publishing house

GR - GREECE

Number of pages

6

Pages from-to

125-130

UT code for WoS article

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EID of the result in the Scopus database

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