Grey-box identification based on horizon estimation and nonlinear optimization, Report no. LiTH-ISY-R-2963
نویسندگان
چکیده
In applications of (nonlinear) model predictive control a more and more common approach for the state estimation is to use moving horizon estimation, which employs (nonlinear) optimization directly on a model for a whole batch of data. This paper shows that horizon estimation may also be used for joint parameter estimation and state estimation, as long as a bias correction based on the Kalman filter is included. A procedure how to approximate the bias correction for nonlinear systems is outlined.
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تاریخ انتشار 2010