Data-driven Modeling of the Toroidal Rotation and Safety Factor Profile Dynamics for AT Scenarios in \mbox{DIII-D}*

First-principle predictive models based on flux averaged transport equations often yield complex expressions not suitable for real-time control. As an alternative to first-principle modeling, data-driven modeling techniques involving system identification have the potential to obtain low-complexity, dynamic models without the need for ad hoc assumptions. This work focuses on the evolution of the toroidal rotation and safety factor profiles in response to magnetic, heating and current-drive systems. Experiments are conducted during the current flattop, in which the actuators are modulated in open-loop to obtain data for the model identification. The plasma profiles are discretized in the spatial coordinate by Galerkin projection. Then a linear model is generated by the prediction error method to relate the rotation and safety factor profiles to the actuators according to a least squares fit.