dc.creator | Carnerero Panduro, Alfonso Daniel | es |
dc.creator | Rodríguez Ramírez, Daniel | es |
dc.creator | Limón Marruedo, Daniel | es |
dc.creator | Alamo, Teodoro | es |
dc.date.accessioned | 2024-04-23T09:43:27Z | |
dc.date.available | 2024-04-23T09:43:27Z | |
dc.date.issued | 2023-05 | |
dc.identifier.citation | Carnerero, A.D., Ramírez, D.R., Limón, D. y Alamo, T. (2023). Kernel-based State-Space Kriging for Predictive Control. IEEE/CAA Journal of Automatica Sinica, 10 (5), 1263-1275. https://doi.org/10.1109/JAS.2023.123459. | |
dc.identifier.issn | 2329-9266 | es |
dc.identifier.uri | https://hdl.handle.net/11441/157012 | |
dc.description | A preliminary version of this paper was presented at the 2022 IEEE Conference on Decision and Control | es |
dc.description.abstract | In this paper, we extend the State-Space Kriging (SSK) modeling technique presented in a previous work by the authors in order to consider non-autonomous systems. SSK is a data-driven method that computes predictions as linear combinations of past outputs. To model the nonlinear dynamics of the system, we propose the Kernel-based State-Space Kriging (K-SSK), a new version of the SSK where kernel functions are used instead of resorting to considerations about the locality of the data. Also, a Kalman filter can be used to improve the predictions at each time step in the case of noisy measurements. A constrained tracking Nonlinear Model Predictive Control (NMPC) scheme using the black-box input-output model obtained by means of the K-SSK prediction method is proposed. Finally, a simulation example and a real experiment are provided in order to assess the performance of the proposed controller. | es |
dc.format | application/pdf | es |
dc.format.extent | 13 p. | es |
dc.language.iso | eng | es |
dc.publisher | Institute of Electric and Electronics Engineers (IEEE) | es |
dc.relation.ispartof | IEEE/CAA Journal of Automatica Sinica, 10 (5), 1263-1275. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Data-driven methods | es |
dc.subject | Model identification | es |
dc.subject | Kernel methods | es |
dc.subject | Predictive Control | es |
dc.title | Kernel-based State-Space Kriging for Predictive Control | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática | es |
dc.relation.projectID | PID2019-106212RB-C41/AEI/10.13039/501100011033 | es |
dc.relation.projectID | P20_00546 | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/10113607 | es |
dc.identifier.doi | 10.1109/JAS.2023.123459 | es |
dc.contributor.group | Universidad de Sevilla. TEP950: Estimación, Predicción, Optimización y Control | es |
dc.journaltitle | IEEE/CAA Journal of Automatica Sinica | es |
dc.publication.volumen | 10 | es |
dc.publication.issue | 5 | es |
dc.publication.initialPage | 1263 | es |
dc.publication.endPage | 1275 | es |
dc.contributor.funder | Agencia Estatal de Investigación. España | es |
dc.contributor.funder | European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) | es |
dc.contributor.funder | Junta de Andalucía | es |