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Data-based robust MPC with componentwise Hoelder kinky inference

Abstract:
The authors have recently developed predictive controllers based on prediction models derived from experimental data, by means of a class of Hölder interpolation called kinky inference. This paper provides a step forward by proposing a novel estimation method based on componentwise Hölder interpolation. This allows to explicitly consider the contribution of each component on each output, yielding better estimations. Following the procedure used in previous works, this estimation method is used to provide a predictor for a nonlinear robust data-based predictive controller, whose performance and robustness is enhanced by the new setting. The properties of the proposed controller are demonstrated in a case study.
Publication status:
Published
Peer review status:
Reviewed (other)

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Publisher copy:
10.1109/CDC40024.2019.9029863

Authors

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-9003-6642


Publisher:
Institute of Electrical and Electronics Engineers
Host title:
2019 IEEE 58th Conference on Decision and Control (CDC)
Pages:
6449-6454
Publication date:
2020-03-12
Acceptance date:
2019-07-19
Event title:
58th IEEE Conference on Decision and Control
Event location:
Nice, France
Event website:
https://cdc2019.ieeecss.org/
Event start date:
2019-12-11
Event end date:
2019-12-13
DOI:
EISBN:
9781728113982
ISBN:
9781728113999


Language:
English
Keywords:
Pubs id:
pubs:1070898
UUID:
uuid:40c27aee-61b1-492d-a392-8d18c8f610cb
Local pid:
pubs:1070898
Source identifiers:
1070898
Deposit date:
2019-11-11
ARK identifier:

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