Predictive Control of Processes with Utilization of Artificial Intelligence Elements

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F20%3A63526196" target="_blank" >RIV/70883521:28140/20:63526196 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.daaam.info/Downloads/Pdfs/proceedings/proceedings_2020/086.pdf" target="_blank" >https://www.daaam.info/Downloads/Pdfs/proceedings/proceedings_2020/086.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2507/31st.daaam.proceedings.086" target="_blank" >10.2507/31st.daaam.proceedings.086</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predictive Control of Processes with Utilization of Artificial Intelligence Elements

Popis výsledku v původním jazyce

Predictive process control is a method of regulation suitable for controlling various types of systems, which is based on the idea of using prediction of future system behavior and its optimization. Normally, a system model is used to predict behavior, and therefore it is necessary for the correct function of predictive control to make its correct selection and determine its parameters so that the controlled system is described as accurately as possible. Another advantage of predictive control is the possibility of including signal restrictions directly in the controller. The aim of the article is the application of some elements of artificial intelligence in suitable areas of predictive control, especially the use of simple evolutionary algorithms in optimization and neural networks as nonlinear models. The possibilities for using these elements are also described here. It is proved that in addition to classical optimization algorithms, it is possible to use simple evolutionary algorithms for optimization of prediction, while the computational complexity can be comparable depending on the type of the solved problem and settings. One chapter of the article deals with the selection of suitable model systems with slow dynamics, their derivation, and the creation of nonlinear models in the form of scalable neural networks. The potential advantage of this approach for the control of systems that are difficult to describe or for the control of systems whose mathematical-physical description is not known was proved in the work. Finally, there are recommended options for deploying the found models on real systems and determining the necessary conditions and requirements for their application.

Název v anglickém jazyce

Predictive Control of Processes with Utilization of Artificial Intelligence Elements

Popis výsledku anglicky

Predictive process control is a method of regulation suitable for controlling various types of systems, which is based on the idea of using prediction of future system behavior and its optimization. Normally, a system model is used to predict behavior, and therefore it is necessary for the correct function of predictive control to make its correct selection and determine its parameters so that the controlled system is described as accurately as possible. Another advantage of predictive control is the possibility of including signal restrictions directly in the controller. The aim of the article is the application of some elements of artificial intelligence in suitable areas of predictive control, especially the use of simple evolutionary algorithms in optimization and neural networks as nonlinear models. The possibilities for using these elements are also described here. It is proved that in addition to classical optimization algorithms, it is possible to use simple evolutionary algorithms for optimization of prediction, while the computational complexity can be comparable depending on the type of the solved problem and settings. One chapter of the article deals with the selection of suitable model systems with slow dynamics, their derivation, and the creation of nonlinear models in the form of scalable neural networks. The potential advantage of this approach for the control of systems that are difficult to describe or for the control of systems whose mathematical-physical description is not known was proved in the work. Finally, there are recommended options for deploying the found models on real systems and determining the necessary conditions and requirements for their application.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Annals of DAAAM and Proceedings of the International DAAAM Symposium

ISBN

978-390273429-7

ISSN

17269679

e-ISSN

—

Počet stran výsledku

6

Strana od-do

626-631

Název nakladatele

DAAAM International Vienna

Místo vydání

Vienna

Místo konání akce

Mostar

Datum konání akce

21. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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