Data-driven Modeling and Feedback Tracking Control of the Toroidal Rotation Profile for Advanced Tokamak Scenarios in DIII-D
W. Wehner, C. Xu, E. Schuster, D. Moreau, D. Mazon, M. L. Walker, D. A. Humphreys and Y. In
IEEE Multi-conference on Systems and Control
Denver, Colorado, September 28-30, 2011
Abstract
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First-principle tokamak plasma predictive models based on flux
averaged transport equations often yield complex expressions not
suitable for real time control implementations. Addition of turbulent
transport phenomena further encum- bers these models with transport
coefficients that must be determined experimentally and the
interdependences between parameters must be accounted for with ad hoc
assumptions. As an alternative to first principle modeling,
data-driven modeling techniques involving system identification have
the potential to obtain practical, low complexity, dynamic models
without the need for ad hoc assumptions. This paper considers the
evolution of the toroidal rotation profile in response to the heating
and current drive (H&CD) systems. Experiments are conducted during
plasma current flattop, in which the actuators are modulated in
open-loop to obtain data for the model identification. The rotation
profile is discretized in the spatial coordinate by Galerkin
projection. Then a linear state space model is generated by the
prediction error method (PEM) to relate the rotation profile to the
actuators according to a least squares fit. An optimal tracking
controller is proposed to regulate the rotation profile to a desired
reference trajectory.