Data-driven modeling and feedback tracking control of the toroidal rotation profile for advanced tokamak scenarios in DIII-D

Abstract

First-principle predictive tokamak plasma models based on flux averaged transport equations often yield complex expressions not suitable for real time control implementations. Addition of turbulent transport phenomena further encumbers 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.