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dc.contributor.authorNgo, Phuong
dc.contributor.authorTejedor Hernandez, Miguel Angel
dc.contributor.authorGodtliebsen, Fred
dc.date.accessioned2022-08-30T08:04:39Z
dc.date.available2022-08-30T08:04:39Z
dc.date.issued2022-02-21
dc.description.abstractThis paper proposes a robust control design method using reinforcement learning for controlling partially-unknown dynamical systems under uncertain conditions. The method extends the optimal reinforcement learning algorithm with a new learning technique based on the robust control theory. By learning from the data, the algorithm proposes actions that guarantee the stability of the closed-loop system within the uncertainties estimated also from the data. Control policies are calculated by solving a set of linear matrix inequalities. The controller was evaluated using simulations on a blood glucose model for patients with Type 1 diabetes. Simulation results show that the proposed methodology is capable of safely regulating the blood glucose within a healthy level under the influence of measurement and process noises. The controller has also significantly reduced the post-meal fluctuation of the blood glucose. A comparison between the proposed algorithm and the existing optimal reinforcement learning algorithm shows the improved robustness of the closed-loop system using our method.en_US
dc.identifier.citationNgo P, Tejedor Hernandez MA, Godtliebsen F. Data-Driven Robust Control Using Reinforcement Learning. Applied Sciences. 2022;12(4)en_US
dc.identifier.cristinIDFRIDAID 2004475
dc.identifier.doi10.3390/app12042262
dc.identifier.issn2076-3417
dc.identifier.urihttps://hdl.handle.net/10037/26467
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.journalApplied Sciences
dc.relation.urihttps://www.mdpi.com/2076-3417/12/4/2262/pdf
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.titleData-Driven Robust Control Using Reinforcement Learningen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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