Towards data-driven stochastic predictive control

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dc.contributor.authorPan, Guanru
dc.contributor.authorOu, Ruchuan
dc.contributor.authorFaulwasser, Timm
dc.date.accessioned2024-12-04T14:55:33Z
dc.date.available2024-12-04T14:55:33Z
dc.date.issued2023-08-02
dc.description.abstractData-driven predictive control based on the fundamental lemma by Willems et al. is frequently considered for deterministic LTI systems subject to measurement noise. However, little has been done on data-driven stochastic control. In this paper, we propose a data-driven stochastic predictive control scheme for LTI systems subject to possibly unbounded additive process disturbances. Based on a stochastic extension of the fundamental lemma and leveraging polynomial chaos expansions, we construct a data-driven surrogate optimal control problem (OCP). Moreover, combined with an online selection strategy of the initial condition of the OCP, we provide sufficient conditions for recursive feasibility and for stability of the proposed data-driven predictive control scheme. Finally, two numerical examples illustrate the efficacy and closed-loop properties of the proposed scheme for process disturbances governed by different distributions.en
dc.identifier.urihttp://hdl.handle.net/2003/43011
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-24844
dc.language.isoen
dc.relation.ispartofseriesInternational journal of robust and nonlinear control
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectclosed-loop propertiesen
dc.subjectdata-driven system representationen
dc.subjectpolynomial chaos expansionen
dc.subjectstochastic model predictive controlen
dc.subject.ddc620
dc.titleTowards data-driven stochastic predictive controlen
dc.typeText
dc.type.publicationtypeResearchArticle
dcterms.accessRightsopen access
eldorado.secondarypublicationtrue
eldorado.secondarypublication.primarycitationPan G, Ou R, Faulwasser T. Towards data-driven stochastic predictive control. Int J Robust Nonlinear Control. 2023; 1-23. doi: 10.1002/rnc.6812
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1002/rnc.6812

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