The use of Neural Network for Nonlinear Predictive Control design for and Overhead Crane
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
The use of Neural Network for Nonlinear Predictive Control design for and Overhead Crane
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
International Conference on Control, Automation and Systems. Volume 2021
ISBN
978-89-93215-21-2
ISSN
2093-7121
e-ISSN
2642-3901
Number of pages
6
Pages from-to
725-730
Publisher name
IEEE
Place of publication
Piscataway
Event location
Čedžu
Event date
Oct 12, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000750950700095