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Robust data-driven predictive control of unknown nonlinear systems using reachability analysis
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.ORCID iD: 0009-0002-3546-8933
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.ORCID iD: 0000-0002-2300-2581
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2023 (English)In: European Journal of Control, ISSN 09473580Article in journal (Refereed) Published
Abstract [en]

This work proposes a robust data-driven predictive control approach for unknown nonlinear systems in the presence of bounded process and measurement noise. Data-driven reachable sets are employed for the controller design instead of using an explicit nonlinear system model. Although the process and measurement noise are bounded, the statistical properties of the noise are not required to be known. By using the past noisy input-output data in the learning phase, we propose a novel method to over-approximate exact reachable sets of an unknown nonlinear system. Then, we propose a data-driven predictive control approach to compute safe and robust control policies from noisy online data. The constraints are guaranteed in the control phase with robust safety margins by effectively using the predicted output reachable set obtained in the learning phase. Finally, a numerical example validates the efficacy of the proposed approach and demonstrates comparable performance with a model-based predictive control approach.

Place, publisher, year, edition, pages
Elsevier BV , 2023.
Keywords [en]
Data-driven methods, Nonlinear systems, Predictive control, Reachability analysis, Zonotopes
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-332094DOI: 10.1016/j.ejcon.2023.100878Scopus ID: 2-s2.0-85165280389OAI: oai:DiVA.org:kth-332094DiVA, id: diva2:1783198
Projects
Cost-and Energy-Efficient Control Systems for BuildingsCLAS—Cybersäkra lärande reglersystemHiSS - Humanizing the Sustainable Smart City, Digital Futures, contract number VF-2020-0260European Union, Horizon Research and Innovation Programme, Marie Skłodowska-Curie grant agreement No. 101062523.
Funder
Swedish Energy Agency, 47859-1Swedish Foundation for Strategic Research, RIT17-0046
Note

QC 20231215

Available from: 2023-07-19 Created: 2023-07-19 Last updated: 2023-12-15Bibliographically approved

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Publisher's full textScopushttps://www-sciencedirect-com.focus.lib.kth.se/science/article/pii/S0947358023001061?utm_campaign=STMJ_AUTH_SERV_PUBLISHED&utm_medium=email&utm_acid=152480019&SIS_ID=&dgcid=STMJ_AUTH_SERV_PUBLISHED&CMX_ID=&utm_in=DM387346&utm_source=AC_

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Farjadnia, MahsaMolinari, MarcoJohansson, Karl H.

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