Implementation and testing of Sigma-Point Kalman filters in Simulink for nonlinear estimation
Fecha
2016Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Trabajo Fin de Grado/Gradu Amaierako Lana
Impacto
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nodoi-noplumx
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Resumen
This 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 s ...
[++]
This 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. [--]
Materias
Nonlinear filtering,
Unscented Kalman filter,
Square root central difference Kalman filter,
Wind Turbine control,
MATLAB/SIMULINK,
CSparse/CXSparse
Titulación
Graduado o Graduada en Ingeniería en Tecnologías Industriales por la Universidad Pública de Navarra /
Industria Teknologietako Ingeniaritzan Graduatua Nafarroako Unibertsitate Publikoan