Extended Kalman Filter Simulink Model for Nonlinear System Modeling Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/4m90dz41d

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  • The objective of the work presented herein is the development of the extended Kalman filter for nonlinear system modeling. A standard Kalman filter is a well-known filter for estimating the state of a system, assuming the system is linear and it has a Gaussian distribution in its noise. In reality, linear systems don't really exist. As a result, the standard Kalman filter is inadequate for modeling most systems. This need could be addressed by changing the standard Kalman filter to work in a nonlinear system. The extended Kalman filter Simulink model proposed in this work allows modeling in nonlinear systems through local linearization. This approach is validated by accurately estimating the nonlinear system behavior for a permanent magnet synchronous motor.
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