The use of Neural Network for Nonlinear Predictive Control design for and Overhead Crane

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10249418" target="_blank" >RIV/61989100:27240/21:10249418 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9649841" target="_blank" >https://ieeexplore.ieee.org/document/9649841</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/ICCAS52745.2021.9649841" target="_blank" >10.23919/ICCAS52745.2021.9649841</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The use of Neural Network for Nonlinear Predictive Control design for and Overhead Crane

Popis výsledku v původním jazyce

The importance of nonlinear model predictive control (NMPC) implementations for industrial processes rises with the increasing of computational power in all hardware units used for regulation and control in practice. However, it assumes a sufficiently accurate model. In case of more complex systems, there might be problem to perform analytical identification. Instead of this, numerical approaches may be deployed with benefit. This paper deals with the design of NMPC for a nonlinear model of an overhead crane using a neural network and compares the solution with the one achieved with the use analytical model of the system. All steps of NMPC design and verification of functionality are performed in Matlab. The paper finally suggests possibility to extend the presented approach for hosting the NMPC algorithm on some real-time embedded target. (C) 2021 ICROS.

Název v anglickém jazyce

The use of Neural Network for Nonlinear Predictive Control design for and Overhead Crane

Popis výsledku anglicky

The importance of nonlinear model predictive control (NMPC) implementations for industrial processes rises with the increasing of computational power in all hardware units used for regulation and control in practice. However, it assumes a sufficiently accurate model. In case of more complex systems, there might be problem to perform analytical identification. Instead of this, numerical approaches may be deployed with benefit. This paper deals with the design of NMPC for a nonlinear model of an overhead crane using a neural network and compares the solution with the one achieved with the use analytical model of the system. All steps of NMPC design and verification of functionality are performed in Matlab. The paper finally suggests possibility to extend the presented approach for hosting the NMPC algorithm on some real-time embedded target. (C) 2021 ICROS.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

International Conference on Control, Automation and Systems. Volume 2021

ISBN

978-89-93215-21-2

ISSN

2093-7121

e-ISSN

2642-3901

Počet stran výsledku

6

Strana od-do

725-730

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Čedžu

Datum konání akce

12. 10. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000750950700095