REAL-TIME ELECTRONIC NEURAL NETWORKS FOR ITER-LIKE MULTIPARAMETER EQUILIBRIUM RECONSTRUCTION AND CONTROL IN COMPASS-D

The plasma position and shape on the COMPASS-D tokamak have been contr olled simultaneously with a 75-kHz bandwidth, hard-wired, real-time ne ural network. The primary network operates with lip to 48 selected mag netic inputs and has been used in the vertical position control loop t o control the position of the upper edge of the plasma at the radios o f a reciprocating Langmuir probe and to keep this constant during a pr ogrammed shape sequence. One of the main advantages of neural networks is their ability to combine signals from different types of diagnosti cs. Two coupled networks are now in use on COMPASS-D. A dedicated soft -X-ray network has been created with inputs from 16 vertical and 16 ho rizontal camera channels. With just four hidden units, it is able to a ccurately determine three output signals defining the plasma core radi us, vertical position, and elongation. These signals are fed to the pr imary network along with selected magnetic inputs and four poloidal fi eld coil control current inputs. The core data are expected to help ch aracterize the equilibrium by providing information on the Shafranov s hift and gradient of elongation, related to the equilibrium parameters beta(p) and l(i). This network, with 15 hidden units, is able to defi ne 10 outputs capable of giving a parameterized display of the plasma boundary. This paper describes results from several networks trained o n various combinations of inputs with (a) simulated inputs and output values, where the precision of the network can be tested; (b) experime ntal inputs and calculated output values, where operational precision can be tested; and (c) hardware networks, where real-time performance can be tested. The results confirm that the neural network method is c apable of giving excellent precision in tokamak boundary reconstructio n but that the necessary accuracy in the experimental inputs for this task is not easily achieved.