Tracking of Current and Rotation Profile Evolution in the \hbox{DIII-D} Tokamak via System Identification

Transport theories produce nonlinear models based on partial differential equations (PDEs) whose complexity often renders them difficult to use for control design. As an alternative, data-driven modeling techniques involving system identification have the potential to obtain practical, low-complexity, dynamic models for the control of plasma systems. The plasma dynamics is first assumed to be governed by a tractable model with unknown and to-be-estimated transport coefficients. After discretizing both in space and time, the system states and to-be-estimated coefficients are 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 estimates not only of the system states but also of the unknown transport coefficients required by the current and rotation feedback controllers.