Transport Parameter Estimations of Plasma Transport Dynamics using the Extended Kalman Filter
C. Xu, Y. Ou and E. Schuster
IEEE Transactions on Plasma Science, vol. 38, no. 3, pp. 359-364, March 2010
Abstract
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The accuracy of first-principle predictive models for the evolution of plasma profiles is sometimes limited by the lack of
understanding of the plasma transport phenomena. It is possible then to develop approximate transport models for the prediction
of plasma dynamics, which are consistent with the available diagnostic data. This data-driven approach, usually referred to as
phenomenological modeling, arises as an alternative to the more classical theory-driven approach. In this paper, we propose a
stochastic filtering approach based on an extended Kalman filter to provide real-time estimates of poorly known or totally
unknown transport coefficients. We first assume that plasma dynamics is governed by tractable models obtained by first principles.
However, the transport parameters are considered unknown and to be estimated. These estimates will be based solely on
input/output diagnostic data and limited understanding of the transport physics. Numerical methods (e.g., finite differences) can
be used to discretize the partial differential equation models both in space and time to obtain finite-dimensional discrete-time
state-space representations. The system states and to-be-estimated parameters are then combined into an augmented state vector.
The resulting nonlinear state-space model is used for the design of an extended Kalman filter that provides real-time estimations
not only of the system states but also of the unknown transport coefficients. Simulation results demonstrate the effectiveness of
the proposed method for a benchmark transport model in cylindrical coordinates.