Particle Swarm Optimisation for Model Predictive Control Adaptation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F23%3A00574863" target="_blank" >RIV/67985556:_____/23:00574863 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/CSCC58962.2023.00030" target="_blank" >http://dx.doi.org/10.1109/CSCC58962.2023.00030</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CSCC58962.2023.00030" target="_blank" >10.1109/CSCC58962.2023.00030</a>
Alternative languages
Result language
angličtina
Original language name
Particle Swarm Optimisation for Model Predictive Control Adaptation
Original language description
This paper is focused on parameter identification for Model Predictive Control (MPC). Two identification techniques for parameters of Auto Regressive model with eXogenous input (ARX model) are considered: namely the identification based on Particle Swarm Optimisation (PSO) and Least Square (LS) method. PSO is investigated and LS is presented in square-root form as a reference method for comparison, respectively. The following points are elaborated and discussed: i) parameters’ estimation of ARX model, ii) design of PSO and LS procedures, iii) design of data-driven MPC algorithm in square-root form, iv) concept of possible use of PSO for semiautomatic fine tuning or retuning of MPC parameters. The proposed theoretical procedures are demonstrated using simply reproducible simulation experiments. Application possibilities are discussed towards robotics and mechatronics.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20204 - Robotics and automatic control
Result continuities
Project
<a href="/en/project/GC23-04676J" target="_blank" >GC23-04676J: Controllable gripping mechanics: Modelling, control and experiments</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 27th International Conference on Circuits, Systems, Communications and Computers - CSCC 2023
ISBN
979-8-3503-3760-0
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
144-149
Publisher name
IEEE
Place of publication
Piscataway
Event location
Rodos
Event date
Jul 19, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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