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Artículo
Componentwise Holder inference for robust learning-based MPC
dc.creator | Manzano Crespo, José María | es |
dc.creator | Muñoz de la Peña Sequedo, David | es |
dc.creator | Calliess, Jan Peter | es |
dc.creator | Limón Marruedo, Daniel | es |
dc.date.accessioned | 2024-01-21T18:37:22Z | |
dc.date.available | 2024-01-21T18:37:22Z | |
dc.date.issued | 2021-11 | |
dc.identifier.citation | Manzano, J.M., Muñoz de la Peña, D., Calliess, J.P. y Limón, D. (2021). Componentwise Holder inference for robust learning-based MPC. IEEE Transactions on Automatic Control, 66 (11), 5577-5583. https://doi.org/10.1109/TAC.2021.3056356. | |
dc.identifier.issn | 0018-9286 | es |
dc.identifier.issn | 1558-2523 | es |
dc.identifier.uri | https://hdl.handle.net/11441/153693 | |
dc.description.abstract | This article presents a novel learning method based on componentwise Holder continuity, which allows one to consider independently the contribution of each input to each output of the function to be learned. The method provides a bounded prediction error, and its learning property is proven. It can be used to obtain a predictor for a nonlinear robust learning-based predictive controller for constrained systems. The resulting controller achieves better closed loop performance and larger domains of attraction than learning methods that only consider nonlinear set membership, as illustrated by a case study. | es |
dc.format | application/pdf | es |
dc.format.extent | 7 p. | es |
dc.language.iso | eng | es |
dc.publisher | IEEE | es |
dc.relation.ispartof | IEEE Transactions on Automatic Control, 66 (11), 5577-5583. | |
dc.subject | Learning systems | es |
dc.subject | Predictive models | es |
dc.subject | Estimation | es |
dc.subject | Uncertainty | es |
dc.subject | Standards | es |
dc.subject | Prediction algorithms | es |
dc.subject | Interpolation | es |
dc.subject | Inference algorithms | es |
dc.subject | Machine learning | es |
dc.subject | Nonlinear systems | es |
dc.subject | Predictive control | es |
dc.subject | Robust stability | es |
dc.title | Componentwise Holder inference for robust learning-based MPC | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
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 | DPI2016-76493-C3-1-R. | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9345441 | es |
dc.identifier.doi | 10.1109/TAC.2021.3056356 | es |
dc.contributor.group | Universidad de Sevilla. TEP950: Estimación, Predicción, Optimización y Control | es |
dc.journaltitle | IEEE Transactions on Automatic Control | es |
dc.publication.volumen | 66 | es |
dc.publication.issue | 11 | es |
dc.publication.initialPage | 5577 | es |
dc.publication.endPage | 5583 | es |
dc.contributor.funder | Agencia Estatal de Investigación. España | es |
dc.contributor.funder | Ministerio de Economia, Industria y Competitividad (MINECO). España | es |
Ficheros | Tamaño | Formato | Ver | Descripción |
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IEEETAC_2021_Manzano_Limon_Com ... | 913.3Kb | [PDF] | Ver/ | Versión aceptada |
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