Title Componentwise Hölder inference for robust learning-based MPC
Authors MANZANO CRESPO, JOSÉ MARÍA, MUÑOZ DE LA PEÑA SEQUEDO, DAVID , Calliess, Jan Peter , LIMÓN MARRUEDO, DANIEL
External publication No
Means IEEE Trans Autom Control
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 1
JCR Impact 6.54900
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100795429&doi=10.1109%2fTAC.2021.3056356&partnerID=40&md5=a02040a245f82a51f12803fdf080bf92
Publication date 01/01/2021
ISI 000711740700053
Scopus Id 2-s2.0-85100795429
DOI 10.1109/TAC.2021.3056356
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,
Keywords Learning systems; Predictive models; Estimation; Uncertainty; Standards; Prediction algorithms; Interpolation; Inference algorithms; machine learning; nonlinear systems; predictive control; robust sta
Universidad Loyola members

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