  In advanced tokamak scenarios, active feedback control of unstable resistive
wall modes (RWM) may be required. A RWM is an instability due to plasma kink at
higher plasma pressure, moderated by the presence of a resistive wall
surrounding the plasma. We address the dominant kink instability associated
with the main nonaxisymmetric (n = 1) RWM, described by the CarMa model. Model
predictive control (MPC) is used, with the aim of enlarging the domain of
attraction of the unstable RWM modes subject to power-supply voltage
constraints. The implementation of MPC is challenging, because the related
quadratic programming (QP) on-line optimization problems must be solved at a
sub-ms sampling rate. Using complexity-reduction pre-processing techniques and
a primal fast gradient method (FGM) QP solver, sufficiently short computation
times for ITER are reachable using a standard personal computer (PC). In this
work we explore even faster finite-word-length (FWL) implementation using
field-programmable gate arrays (FPGA), which would facilitate experimental
testing of such control algorithms on dynamically faster medium-sized tokamaks,
and compare the computational accuracy and time with the PC implementation.
