Developement of real time diagnostics and feedback algorithms for JET in view of the next step

Real time control of many plasma parameters will be an essential aspect in the development of reliable high performance operation of Next Step Tokamaks. The main prerequisites for any feedback scheme are the precise real-time determination of the quantities to be controlled, requiring top quality and highly reliable diagnostics, and the availability of robust control algorithms. A new set of real time diagnostics was recently implemented on JET to prove the feasibility of determining, with high accuracy and time resolution, the most important plasma quantities. With regard to feedback algorithms, new model–based controllers were developed to allow a more robust control of several plasma parameters. Both diagnostics and algorithms were successfully used in several experiments, ranging from H-mode plasmas to configuration with ITBs. Since elaboration of computationally heavy measurements is often required, significant attention was devoted to non-algorithmic methods like Digital or Cellular Neural/Nonlinear Networks. The real time hardware and software adopted architectures are also described with particular attention to their relevance to ITER.Comment: 12th International Congress on Plasma Physics, 25-29 October 2004, Nice (France

A neural network approach for the detection of the locking position in RFX

In the FFX (reversed field experiment), one of the most important reversed field pinch (RFP) devices in the fusion community, wall locked modes have always been present. Recently, a new technique has demonstrated the possibility of inducing a continuous rotation of the modes with respect to the wall. The non-linear coupling of the m=0 and m=1 modes has been used to decouple the modes themselves, and the mode rotation has been induced by means of a pre-programmed waveform of a toroidal magnetic field rotating ripple. Consequently, a feedback system for detecting the locked mode position along the toroidal co-ordinate and able to create a continuous rotation with variable speed has been envisaged. Neural networks (NNs) represent a promising approach for rapid detection of the locked mode angular position in such a system, and in this paper the performances of different NNs trained to identify the locked mode position are compared and discussed. In particular, their robustness to noise is analyzed, and it is shown that NNs provide reliable results, sometimes better than those computed with fourier analysis