REAL-TIME CONTROL OF A TOKAMAK PLASMA USING NEURAL NETWORKS

In this paper we present results from the first use of neural networks for real-time control of the high-temperature plasma in a tokamak fus ion experiment. The tokamak is currently the principal experimental de vice for research into the magnetic confinement approach to controlled fusion. In an effort to improve the energy confinement properties of the high-temperature plasma inside tokamaks, recent experiments have f ocused on the use of noncircular cross-sectional plasma shapes. Howeve r, the accurate generation of such plasmas represents a demanding prob lem involving simultaneous control of several parameters on a time sca le as short as a few tens of microseconds. Application of neural netwo rks to this problem requires fast hardware, for which we have develope d a fully parallel custom implementation of a multilayer perceptron, b ased on a hybrid of digital and analogue techniques.