Robust learning-based MPC for nonlinear constrained systems
This paper presents a robust learning-based predictive control strategy for nonlinear systems subject to both input and output constraints, under the assumption that the model function is not known a priori and only input–output data are available. The proposed controller is obtained using a nonpara...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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Elsevier
2020
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| _version_ | 1797096914451169280 |
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| author | Manzano, JM Limon, D Muñoz de la Peña, D Calliess, J-P |
| author_facet | Manzano, JM Limon, D Muñoz de la Peña, D Calliess, J-P |
| author_sort | Manzano, JM |
| collection | OXFORD |
| description | This paper presents a robust learning-based predictive control strategy for nonlinear systems subject to both input and output constraints, under the assumption that the model function is not known a priori and only input–output data are available. The proposed controller is obtained using a nonparametric machine learning technique to estimate a prediction model. Based on this prediction model, a novel stabilizing robust predictive controller without terminal constraint is proposed. The design procedure is purely based on data and avoids the estimation of any robust invariant set, which is in general a hard task. The resulting controller has been validated in a simulated case study. |
| first_indexed | 2024-03-07T04:48:16Z |
| format | Journal article |
| id | oxford-uuid:d411638e-5ff9-4606-b088-5932b17c4cce |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T04:48:16Z |
| publishDate | 2020 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:d411638e-5ff9-4606-b088-5932b17c4cce2022-03-27T08:15:44ZRobust learning-based MPC for nonlinear constrained systemsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:d411638e-5ff9-4606-b088-5932b17c4cceEnglishSymplectic ElementsElsevier2020Manzano, JMLimon, DMuñoz de la Peña, DCalliess, J-PThis paper presents a robust learning-based predictive control strategy for nonlinear systems subject to both input and output constraints, under the assumption that the model function is not known a priori and only input–output data are available. The proposed controller is obtained using a nonparametric machine learning technique to estimate a prediction model. Based on this prediction model, a novel stabilizing robust predictive controller without terminal constraint is proposed. The design procedure is purely based on data and avoids the estimation of any robust invariant set, which is in general a hard task. The resulting controller has been validated in a simulated case study. |
| spellingShingle | Manzano, JM Limon, D Muñoz de la Peña, D Calliess, J-P Robust learning-based MPC for nonlinear constrained systems |
| title | Robust learning-based MPC for nonlinear constrained systems |
| title_full | Robust learning-based MPC for nonlinear constrained systems |
| title_fullStr | Robust learning-based MPC for nonlinear constrained systems |
| title_full_unstemmed | Robust learning-based MPC for nonlinear constrained systems |
| title_short | Robust learning-based MPC for nonlinear constrained systems |
| title_sort | robust learning based mpc for nonlinear constrained systems |
| work_keys_str_mv | AT manzanojm robustlearningbasedmpcfornonlinearconstrainedsystems AT limond robustlearningbasedmpcfornonlinearconstrainedsystems AT munozdelapenad robustlearningbasedmpcfornonlinearconstrainedsystems AT calliessjp robustlearningbasedmpcfornonlinearconstrainedsystems |