Probabilistic performance validation of deep learning-based robust NMPC controllers

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dc.contributor.authorKarg, Benjamin
dc.contributor.authorAlamo, Teodoro
dc.contributor.authorLucia, Sergio
dc.date.accessioned2022-03-23T13:50:20Z
dc.date.available2022-03-23T13:50:20Z
dc.date.issued2021-07-22
dc.description.abstractSolving nonlinear model predictive control problems in real time is still an important challenge despite of recent advances in computing hardware, optimization algorithms and tailored implementations. This challenge is even greater when uncertainty is present due to disturbances, unknown parameters or measurement and estimation errors. To enable the application of advanced control schemes to fast systems and on low-cost embedded hardware, we propose to approximate a robust nonlinear model controller using deep learning and to verify its quality using probabilistic validation techniques. We propose a probabilistic validation technique based on finite families, combined with the idea of generalized maximum and constraint backoff to enable statistically valid conclusions related to general performance indicators. The potential of the proposed approach is demonstrated with simulation results of an uncertain nonlinear system.en
dc.identifier.urihttp://hdl.handle.net/2003/40818
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-22675
dc.language.isoende
dc.relation.ispartofseriesInternational journal of robust and nonlinear control;31(18)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectMachine learningen
dc.subjectModel predictive controlen
dc.subjectProbabilistic validationen
dc.subjectRobust controlen
dc.subject.ddc660
dc.titleProbabilistic performance validation of deep learning-based robust NMPC controllersen
dc.typeTextde
dc.type.publicationtypearticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationKarg, B, Alamo, T, Lucia, S. Probabilistic performance validation of deep learning-based robust NMPC controllers. Int J Robust Nonlinear Control. 2021; 31: 8855– 8876. https://doi.org/10.1002/rnc.5696de
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1002/rnc.5696de

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