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Robust Data-Driven Predictive Control of Unknown Nonlinear Systems Using Reachability Analysis
KTH, Skolan för industriell teknik och management (ITM), Energiteknik, Tillämpad termodynamik och kylteknik.ORCID-id: 0009-0002-3546-8933
Jacobs University Bremen, Bremen, Germany.
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, USA.
KTH, Skolan för industriell teknik och management (ITM), Energiteknik, Tillämpad termodynamik och kylteknik.ORCID-id: 0000-0002-2300-2581
Vise andre og tillknytning
2023 (engelsk)Konferansepaper, Oral presentation with published abstract (Fagfellevurdert)
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 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 through the effective use of 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.

sted, utgiver, år, opplag, sider
2023.
Emneord [en]
Predictive control for nonlinear systems, Robust control
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-336528OAI: oai:DiVA.org:kth-336528DiVA, id: diva2:1796563
Konferanse
European Control Conference 2023, 13 - 16 June, 2023, Bucharest, Romania
Prosjekter
Cost- and Energy-Efficient Control Systems for BuildingsCLAS—Cybersäkra lärande reglersystemHiSS—Humanizing the Sustainable Smart CityMarie Skłodowska- Curie
Forskningsfinansiär
Swedish Energy Agency, 47859-1Swedish Foundation for Strategic Research, RIT17-0046EU, Horizon Europe, 101062523EU, Horizon Europe, 830927
Merknad

QC 20230918

Tilgjengelig fra: 2023-09-12 Laget: 2023-09-12 Sist oppdatert: 2023-09-18bibliografisk kontrollert

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Farjadnia, MahsaMolinari, MarcoJohansson, Karl H.

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