Results obtained in the initial experimental phase of Heliotron J are reported. Electron beam mapping of the magnetic surfaces at a reduced DC magnetic field has revealed that the observed surfaces are in basic agreement with the ones calculated on the basis of the measured ambient field around the device. For 53.2 GHz second harmonic ECH hydrogen plasmas, a fairly wide resonance range for breakdown and heating by the TE02 mode has been observed in Heliotron J as compared with that in Heliotron E. With ECH injection powers up to ≈ 400 kW, diamagnetic stored energies up to ≈ 0.7 kJ were obtained without optimized density control.

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Studies of global energy confinement and toroidal plasma current behaviour for the second harmonic 70 GHz ECH at B = 1-1.5 T are described with emphasis on the magnetic configuration effects in the helical-axis heliotron 'Heliotron J'. At low densities of ne < 0.4×10 19 m −3 , the electron temperature reached T e ≈ 1 keV in the core region, indicating the production of collision-less plasmas of electron collisionality ν * 0.1, where ν * = ν/(v e /π R 0 q). For medium densities of 0.5 × 10 19 m −3 < ne < 2 × 10 19 m −3 , the preferable energy confinement time, 1.5-2 times larger than that of the ISS95 scaling, was obtained under the condition of localized central heating at B ≈ 1.25 T for the standard configuration of Heliotron J. The measurements of the toroidal current under perpendicular microwave injection revealed the change of the current flow direction as a function of the poloidal magnetic field. The measured current behaviour was found to be qualitatively consistent with that of the bootstrap current predicted from neoclassical theory. The observed flow reversal showed that a proper selection of the field configuration could control the bootstrap current in the helical-axis heliotron. In addition, the current control through the electron cyclotron current drive scenario with oblique injection of microwaves was experimentally examined.

An extended self-organizing map (ESOM) network, which consists of a self-organization phase and an optimization phase, was recently developed to construct a local model network (LMN) automatically using the plant data. However, this previous result suffers two drawbacks: (1) increased computation time in the self-organization phase as the number of local models increases, (2) lack of checking stability conditions for both local models and LMN. To overcome these problems, an improved algorithm for the ESOM network is developed in this paper by employing a competitive learning algorithm for data clustering in the self-organization phase and parametric constraints are formulated in the optimization phase to handle the stability of local models. In addition, the global stability of LMN is addressed. With LMN constructed by the ESOM network, it serves as a basis for building a nonlinear controller that combines several local controllers through the weighting functions obtained by the ESOM algorithm. Literature examples are used to illustrate the proposed ESOM-based modeling and controller design method.