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On parameter and state estimation for linear differential-algebraic equations
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DOI：10.1016/j.automatica.2006.09.016.png)
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Abstract
En 中文
The current demand for more complex models has initiated a shift away from state-space models towards models described by differential-algebraic equations (DAEs). These models arise as the natural product of object-oriented modeling languages, such as Modelica. However, the mathematics of DAEs is somewhat more involved than the standard state-space theory. The aim of this work is to present a well-posed description of a linear stochastic differential-algebraic equation and more importantly explain how well-posed estimation problems can be formed. We will consider both the system identification problem and the state estimation problem. Besides providing the necessary theory we will also explain how the procedures can be implemented by means of efficient numerical methods. (C) 2007 Elsevier Ltd. All rights reserved.
Keywords:
differential-algebraic equations
Kalman filtering
gray-box models
estimation
parameter estimation
state estimation
stochastic differential-algebraic equations
modeling
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