Publication:
Implementation and testing of Sigma-Point Kalman filters in Simulink for nonlinear estimation

dc.contributor.advisorTFERitter, Bastian
dc.contributor.advisorTFEKonigorski, Ulrich
dc.contributor.affiliationEscuela Técnica Superior de Ingenieros Industriales y de Telecomunicaciónes_ES
dc.contributor.affiliationTelekomunikazio eta Industria Ingeniarien Goi Mailako Eskola Teknikoaeu
dc.contributor.authorIriarte Arrese, Imanol
dc.date.accessioned2017-11-13T09:18:26Z
dc.date.available2017-11-13T09:18:26Z
dc.date.issued2016
dc.description.abstractThis thesis discusses the implementation of Sigma-Point Kalman Filters (SPKF) for state estimation of nonlinear wind turbine systems. First, a theoretical review of nonlinear Kalman filtering is given. Then the different ways of implementing the algorithms and testing them in SIMULINK are discussed and eventually the developed algorithms are explained and illustrative results from nonlinear simulations are presented. This work confirms that the linear Kalman Filter can be efficiently extended to nonlinear systems by means of Sigma-Point Kalman Filters such as the Unscented Kalman Filter and the Central Difference Kalman Filter. It is also shown that the performance of the square-root implementations available for SPKF is as accurate as that of the original ones, even if they are more computationally efficient algorithms.en
dc.description.degreeGraduado o Graduada en Ingeniería en Tecnologías Industriales por la Universidad Pública de Navarraes_ES
dc.description.degreeIndustria Teknologietako Ingeniaritzan Graduatua Nafarroako Unibertsitate Publikoaneu
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/26128
dc.language.isoengen
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.subjectNonlinear filteringen
dc.subjectUnscented Kalman filteren
dc.subjectSquare root central difference Kalman filteren
dc.subjectWind Turbine controlen
dc.subjectMATLAB/SIMULINKen
dc.subjectCSparse/CXSparseen
dc.titleImplementation and testing of Sigma-Point Kalman filters in Simulink for nonlinear estimationes_ES
dc.typeinfo:eu-repo/semantics/bachelorThesis
dspace.entity.typePublication

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