Article
Componentwise Holder inference for robust learning-based MPC
Author/s | Manzano Crespo, José María
Muñoz de la Peña Sequedo, David Calliess, Jan Peter Limón Marruedo, Daniel |
Department | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Publication Date | 2021-11 |
Deposit Date | 2024-01-21 |
Published in |
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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 ... 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. |
Funding agencies | Agencia Estatal de Investigación. España Ministerio de Economia, Industria y Competitividad (MINECO). España |
Project ID. | PID2019-106212RB-C41/AEI/10.13039/501100011033
DPI2016-76493-C3-1-R. |
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. |
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