DC FieldValueLanguage
dc.contributor.authorEckel, Christina-
dc.contributor.authorMaiworm, Michael-
dc.contributor.authorFindeisen, Rolf-
dc.date.accessioned2022-09-20T06:47:07Z-
dc.date.available2022-09-20T06:47:07Z-
dc.date.issued2022-06-
dc.identifier.citationAmerican Control Conference (ACC 2022)de_DE
dc.identifier.isbn978-1-6654-5196-3de_DE
dc.identifier.urihttp://hdl.handle.net/11420/13630-
dc.description.abstractControlling wind kites requires accurate models, both for safe operation, as well as for thrust maximization. To this end, we present a trajectory tracking model predictive control (MPC) approach in combination with Gaussian processes for model learning. Since perfect prediction models are usually unavailable, we use a hybrid model approach consisting of a physical base model extended by Gaussian processes that learn the model-plant mismatch. This allows for the computation of optimized improved reference trajectories, compared to the nominal model case. We furthermore outline an online-learning trajectory tracking MPC approach, which updates the process model recursively taking new measurements into account if the prediction error becomes too large. In simulations we show that even for large model-plant mismatches correct and safe operation can be achieved using the hybrid model in the MPC.en
dc.language.isoende_DE
dc.publisherIEEEde_DE
dc.subjectTrajectory trackingde_DE
dc.subjectComputational modelingde_DE
dc.subjectMeasurement uncertaintyde_DE
dc.subjectTraining datade_DE
dc.subjectGaussian processesde_DE
dc.subjectPredictive modelsde_DE
dc.subjectStability analysisde_DE
dc.subject.ddc004: Informatikde_DE
dc.subject.ddc510: Mathematikde_DE
dc.subject.ddc600: Technikde_DE
dc.subject.ddc620: Ingenieurwissenschaftende_DE
dc.titleOptimal operation and control of towing kites using online and offline Gaussian process learning supported model predictive controlde_DE
dc.typeArticlede_DE
dc.type.diniarticle-
dcterms.DCMITypeText-
tuhh.abstract.englishControlling wind kites requires accurate models, both for safe operation, as well as for thrust maximization. To this end, we present a trajectory tracking model predictive control (MPC) approach in combination with Gaussian processes for model learning. Since perfect prediction models are usually unavailable, we use a hybrid model approach consisting of a physical base model extended by Gaussian processes that learn the model-plant mismatch. This allows for the computation of optimized improved reference trajectories, compared to the nominal model case. We furthermore outline an online-learning trajectory tracking MPC approach, which updates the process model recursively taking new measurements into account if the prediction error becomes too large. In simulations we show that even for large model-plant mismatches correct and safe operation can be achieved using the hybrid model in the MPC.de_DE
tuhh.publisher.doi10.23919/ACC53348.2022.9867371-
tuhh.publication.instituteElektrische Energietechnik E-6de_DE
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.type.driverarticle-
dc.type.casraiJournal Article-
tuhh.container.startpage2637de_DE
tuhh.container.endpage2643de_DE
dc.relation.conferenceAmerican Control Conference, ACC 2022de_DE
dc.identifier.scopus2-s2.0-85138489869de_DE
local.status.inpressfalsede_DE
datacite.resourceTypeArticle-
datacite.resourceTypeGeneralJournalArticle-
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.creatorOrcidEckel, Christina-
item.creatorOrcidMaiworm, Michael-
item.creatorOrcidFindeisen, Rolf-
item.creatorGNDEckel, Christina-
item.creatorGNDMaiworm, Michael-
item.creatorGNDFindeisen, Rolf-
item.openairetypeArticle-
item.grantfulltextnone-
item.languageiso639-1en-
item.mappedtypeArticle-
crisitem.author.deptElektrische Energietechnik E-6-
crisitem.author.orcid0000-0003-4048-3548-
crisitem.author.orcid0000-0001-8129-4132-
crisitem.author.orcid0000-0002-9112-5946-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik (E)-
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