Simultaneous iterative learning control of mode entrainment and error field

Abstract

It is shown that static error fields (EFs) can severely limit the maximum rotation frequency achievable in mode entrainment by applied rotating fields. It is also shown that the rotation non-uniformities caused by an EF can be used to diagnose and correct said EF in real time. Simulations using typical DIII-D data show that this can be achieved within a small number of mode rotation periods by an iterative learning control algorithm. In addition to rapidly correcting the EF, this gives access to high entrainment frequencies that would not be accessible otherwise, and paves the way to rotational stabilization and improved mode control.

Calculating RF current condensation with self-consistent ray-tracing

“…The island's phase is locked such that the ray goes through its O-point. Adjustment of a locked island's phase to deposit power at the island's O-point has been achieved in DIII-D with external magnetic perturbations [54][55][56] .…”

Data-Driven Control Based on Information Concentration Estimator and Regularized Online Sequential Extreme Learning Machine

“…Data-driven control (DDC) algorithms rely on the input data and output data of control systems, and are very suitable for some nonlinear systems whose mechanism models cannot be established. After several years of development, some data-driven control techniques have been investigated, such as model free adaptive control (MFAC) [1][2][3][4][5], iterative learning control (ILC) [6][7][8][9], iterative feedback tuning (IFT) [10,11], unfalsified control (UC) [12,13], virtual reference feedback tuning (VRFT) [14], lazy learning control [15], some control algorithms based on neural networks [16][17][18][19], reinforcement learning [20][21][22][23][24], and so on. These DDC methods can be divided into two fundamental categories.…”