| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0574863 |
| utime |
20240402214328.7 |
| mtime |
20230827235959.9 |
| SCOPUS |
85182744825 |
| DOI |
10.1109/CSCC58962.2023.00030 |
| title
(primary) (eng) |
Particle Swarm Optimisation for Model Predictive Control Adaptation |
| specification |
| page_count |
6 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0574861 |
| ISBN |
979-8-3503-3760-0 |
| title
|
Proceedings of the 27th International Conference on Circuits, Systems, Communications and Computers - CSCC 2023 |
| page_num |
144-149 |
| publisher |
| place |
Piscataway |
| name |
IEEE |
| year |
2023 |
|
| editor |
| name1 |
Mastorakis |
| name2 |
Nikos |
|
|
| keyword |
data-driven modelling |
| keyword |
parameter estimation |
| keyword |
particle swarm optimisation |
| keyword |
predictive control |
| author
(primary) |
| ARLID |
cav_un_auth*0101064 |
| name1 |
Belda |
| name2 |
Květoslav |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| full_dept |
Department of Adaptive Systems |
| garant |
K |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0382598 |
| name1 |
Kuklišová Pavelková |
| name2 |
Lenka |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GC23-04676J |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0453493 |
|
| abstract
(eng) |
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. |
| action |
| ARLID |
cav_un_auth*0453492 |
| name |
International Conference on Circuits, Systems, Communications and Computers (CSCC 2023) /27./ |
| dates |
20230719 |
| mrcbC20-s |
20230722 |
| place |
Rodos |
| country |
GR |
|
| RIV |
BC |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20204 |
| reportyear |
2024 |
| num_of_auth |
2 |
| presentation_type |
ZP |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0344802 |
| confidential |
S |
| arlyear |
2023 |
| mrcbU14 |
85182744825 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0574861 Proceedings of the 27th International Conference on Circuits, Systems, Communications and Computers - CSCC 2023 IEEE 2023 Piscataway 144 149 979-8-3503-3760-0 |
| mrcbU67 |
Mastorakis Nikos 340 |
|