Rapid Adaptive Control via Fused Machine Learning and Receding Horizon Techniques: Theoretical Framework and Application to Renewable Energy-Harvesting Systems

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dc.contributor.advisorChristopher Vermillion, Chair
dc.contributor.advisorKenneth Granlund, Member
dc.contributor.advisorAndre Mazzoleni, Member
dc.contributor.advisorScott Ferguson, Member
dc.contributor.advisorIqbal Husain, Graduate School Representative
dc.contributor.authorSiddiqui, Ayaz
dc.date.accepted2022-01-05
dc.date.accessioned2022-02-08T13:30:17Z
dc.date.available2022-02-08T13:30:17Z
dc.date.defense2021-12-09
dc.date.issued2021-12-09
dc.date.released2022-02-08
dc.date.reviewed2021-12-15
dc.date.submitted2021-12-09
dc.degree.disciplineMechanical Engineering
dc.degree.leveldissertation
dc.degree.nameDoctor of Philosophy
dc.descriptionNorth Carolina State University Theses Mechanical and Aerospace Engineering.
dc.formatPh.D. North Carolina State University, 2022.
dc.identifier.otherdeg27910
dc.identifier.urihttps://www.lib.ncsu.edu/resolver/1840.20/39401
dc.titleRapid Adaptive Control via Fused Machine Learning and Receding Horizon Techniques: Theoretical Framework and Application to Renewable Energy-Harvesting Systems
dcterms.extent1 online resource (ix, 93 pages) : illustrations

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