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A Neural Network Approach to Tokamak Equilibrium Control

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Neural Network Applications

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

Abstract

We exploit the properties of the multilayer perceptron to develop a neural network approach to the feedback control of plasma position and shape in a tokamak experiment. The requirements of large bandwidth and high precision have led us to develop a custom hybrid analogue-digital hardware implementation of the neural network using conventional components. It is planned to demonstrate a complete system on the COMPASS tokamak at Culham Laboratory.

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References

  1. Bishop C M, Strachan I G D, O’Rourke J, Maddison G P and Thomas P R (1992) Reconstruction of Tokamak Density Profiles using Feedforward Networks Neural Computing and Applications 1 No 1.

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  2. Braams B J, Jilge W and Lackner K (1986) Fast Determination of Plasma Parameters Through Function Parameterisation, Nuclear Fusion 26 No 6, 699.

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  3. Hofmann F and Tonetti G (1988) Fast Identification of Plasma Boundary and X-Points in Elongated Tokamaks, Nuclear Fusion 28 No 3, 519.

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  6. Osborne T H, Fukumoto H, Hosogane N et al (1986) Plasma Shape and Position Control on DIIID, Bull. Am. Phys. Soc. (1986) 31 No 9, 1502.

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Author information

Authors and Affiliations

  1. Harwell Laboratory, AEA Technology, Oxfordshire, OX11 0RA, UK

    Chris Bishop

  2. Culham Laboratory, AEA Technology, Oxfordshire, OX14 3DB, UK

    Peter Cox, Paul Haynes, Colin Roach, Mike Smith, Tom Todd & David Trotman

Authors
  1. Chris Bishop
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  2. Peter Cox
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  3. Paul Haynes
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  4. Colin Roach
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  5. Mike Smith
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  6. Tom Todd
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  7. David Trotman
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Editor information

Editors and Affiliations

  1. Centre for Neural Networks, Department of Mathematics, King’ College, WC2R 2LS, Strand, London, UK

    J. G. Taylor BA, BSc, MA, PhD, FlnstP (Director) (Director)

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© 1992 Springer-Verlag London Limited

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Cite this paper

Bishop, C. et al. (1992). A Neural Network Approach to Tokamak Equilibrium Control. In: Taylor, J.G. (eds) Neural Network Applications. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-2003-2_9

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  • DOI: https://doi.org/10.1007/978-1-4471-2003-2_9

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19772-0

  • Online ISBN: 978-1-4471-2003-2

  • eBook Packages: Springer Book Archive

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