  In this work we demostrate using a two-temperature volume average 0D model
that robust stabilization, with regard the hellium ash confinement time, of the
burn conditions of a tokamak reactor with the ITER FEAT design parameters can
be achieved using Radial Basis Neural Networks (RBNN). Alpha particle
thermalization time delay is taken into account in this model. The control
actions implemented by means of a RBNN, include the modulation of the DT
refueling rate, a neutral He-4 injection beam and auxiliary heating powers to
ions and to electrons; all of them constrained to lie within allowable range
values. Here we assume that the tokamak follows the IPB98(y,2) scaling for the
energy confinement time, while the helium ash confinement time is assumed to be
independently estimated on-line. The DT and helium ash confinement times are
assumed to keep a constant relationship at all times. An on-line noisy
estimation of the helium ash confinement time is simulated by corrupting it
with pseudo Gaussian noise.

Plasma Physics