Počet záznamů: 1
Particle Swarm Optimisation for Model Predictive Control Adaptation
- 1.0574863 - ÚTIA 2024 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Belda, Květoslav - Kuklišová Pavelková, Lenka
Particle Swarm Optimisation for Model Predictive Control Adaptation.
Proceedings of the 27th International Conference on Circuits, Systems, Communications and Computers - CSCC 2023. Piscataway: IEEE, 2023 - (Mastorakis, N.), s. 144-149. ISBN 979-8-3503-3760-0.
[International Conference on Circuits, Systems, Communications and Computers (CSCC 2023) /27./. Rodos (GR), 19.07.2023-22.07.2023]
Grant CEP: GA ČR(CZ) GC23-04676J
Institucionální podpora: RVO:67985556
Klíčová slova: data-driven modelling * parameter estimation * particle swarm optimisation * predictive control
Obor OECD: Robotics and automatic control
http://library.utia.cas.cz/separaty/2023/AS/belda-0574863.pdf
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.
Trvalý link: https://hdl.handle.net/11104/0344802
Počet záznamů: 1