Distributed predictive control based on Gaussian process models

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creativework.keywordsDistributed control, Interconnected systems, Predictive control, Stochastic systems
creativework.publisherElsevier Ltden
dc.contributor.authorGrancharova A.
dc.contributor.authorValkova I.
dc.contributor.authorHvala N.
dc.contributor.authorKocijan J.
dc.date.accessioned2024-07-10T14:27:05Z
dc.date.accessioned2024-07-10T14:50:32Z
dc.date.available2024-07-10T14:27:05Z
dc.date.available2024-07-10T14:50:32Z
dc.date.issued2023-03-01
dc.description.abstractA suboptimal approach to distributed NMPC is proposed based on Gaussian process models of the interconnected systems dynamics and taking into account the imposed constraints. The suggested method is based on a sequential linearization of the nonlinear system dynamics and finding a suboptimal solution of the resulting Quadratic Programming (QP) problem by using distributed iterations of the dual accelerated gradient method. The main advantages of the distributed approach are that it allows the computation of the suboptimal control inputs to be done autonomously by the subsystems without the need for centralized optimization and it has a simple software implementation. The proposed method is illustrated with simulations on the simplified model of a sewer system.
dc.identifier.doi10.1016/j.automatica.2022.110807
dc.identifier.issn0005-1098
dc.identifier.scopusSCOPUS_ID:85145257275en
dc.identifier.urihttps://rlib.uctm.edu/handle/123456789/771
dc.language.isoen
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85145257275&origin=inward
dc.titleDistributed predictive control based on Gaussian process models
dc.typeArticle
oaire.citation.volume149
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