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Particle Swarm Optimisation for Model Predictive Control Adaptation
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SYSNO ASEP 0574863 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Particle Swarm Optimisation for Model Predictive Control Adaptation Author(s) Belda, Květoslav (UTIA-B) RID, ORCID
Kuklišová Pavelková, Lenka (UTIA-B) ORCIDNumber of authors 2 Source Title Proceedings of the 27th International Conference on Circuits, Systems, Communications and Computers - CSCC 2023. - Piscataway : IEEE, 2023 / Mastorakis Nikos - ISBN 979-8-3503-3760-0 Pages s. 144-149 Number of pages 6 s. Publication form Print - P Action International Conference on Circuits, Systems, Communications and Computers (CSCC 2023) /27./ Event date 19.07.2023 - 22.07.2023 VEvent location Rodos Country GR - Greece Event type WRD Language eng - English Country US - United States Keywords data-driven modelling ; parameter estimation ; particle swarm optimisation ; predictive control Subject RIV BC - Control Systems Theory OECD category Robotics and automatic control R&D Projects GC23-04676J GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 EID SCOPUS 85182744825 DOI 10.1109/CSCC58962.2023.00030 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2024
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