STABILIZATION OF BURN CONDITIONS IN A THERMONUCLEAR REACTOR USING ARTIFICIAL NEURAL NETWORKS

In this work we develop an artificial neural network (ANN) for the fee dback stabilization of a thermonuclear reactor at nearly ignited burn conditions. A volume-averaged zero-dimensional nonlinear model is used to represent the time evolution of the electron density, the relative density of alpha particles and the temperature of the plasma, where a particular scaling law for the energy confinement time previously use d by other authors, was adopted. The control actions include the concu rrent modulation of the D-T refuelling rate, the injection of a neutra l He-4 beam and an auxiliary heating power modulation, which are const rained to take values within a maximum and minimum levels. For this pu rpose a feedforward multilayer artificial neural network with sigmoida l activation function is trained using a back-propagation through-time technique. Numerical examples are used to illustrate the behaviour of the resulting ANN-dynamical system configuration. It is concluded tha t the resulting ANN can successfully stabilize the nonlinear model of the thermonuclear reactor at nearly ignited conditions for temperature and density departures significantly far from their nominal operating values. The NN-dynamical system configuration is shown to be robust w ith respect to the thermalization time of the alpha particles for pert urbations within the region used to train the NN.