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Real Time Hybrid Model Predictive Control for the Current Profile of the Tokamak à Configuration Variable (TCV)

Author

Listed:
  • Izaskun Garrido

    (Faculty of Engineering, University of the Basque Country (UPV/EHU), Paseo Rafael Moreno 3, Bilbao 48013, Spain)

  • Aitor J. Garrido

    (Faculty of Engineering, University of the Basque Country (UPV/EHU), Paseo Rafael Moreno 3, Bilbao 48013, Spain)

  • Stefano Coda

    (Centre de Recherches en Physique des Plasmas, École Polytechnique Fédérale de Lausanne (CRPP-EPFL), CH-1015 Lausanne, Switzerland)

  • Hoang B. Le

    (Centre de Recherches en Physique des Plasmas, École Polytechnique Fédérale de Lausanne (CRPP-EPFL), CH-1015 Lausanne, Switzerland)

  • Jean Marc Moret

    (Centre de Recherches en Physique des Plasmas, École Polytechnique Fédérale de Lausanne (CRPP-EPFL), CH-1015 Lausanne, Switzerland)

Abstract

Plasma stability is one of the obstacles in the path to the successful operation of fusion devices. Numerical control-oriented codes as it is the case of the widely accepted RZIp may be used within Tokamak simulations. The novelty of this article relies in the hierarchical development of a dynamic control loop. It is based on a current profile Model Predictive Control (MPC) algorithm within a multiloop structure, where a MPC is developed at each step so as to improve the Proportional Integral Derivative (PID) global scheme. The inner control loop is composed of a PID-based controller that acts over the Multiple Input Multiple Output (MIMO) system resulting from the RZIp plasma model of the Tokamak à Configuration Variable (TCV). The coefficients of this PID controller are initially tuned using an eigenmode reduction over the passive structure model. The control action corresponding to the state of interest is then optimized in the outer MPC loop. For the sake of comparison, both the traditionally used PID global controller as well as the multiloop enhanced MPC are applied to the same TCV shot. The results show that the proposed control algorithm presents a superior performance over the conventional PID algorithm in terms of convergence. Furthermore, this enhanced MPC algorithm contributes to extend the discharge length and to overcome the limited power availability restrictions that hinder the performance of advanced tokamaks.

Suggested Citation

  • Izaskun Garrido & Aitor J. Garrido & Stefano Coda & Hoang B. Le & Jean Marc Moret, 2016. "Real Time Hybrid Model Predictive Control for the Current Profile of the Tokamak à Configuration Variable (TCV)," Energies, MDPI, vol. 9(8), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:8:p:609-:d:75257
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    References listed on IDEAS

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    7. Sevillano, M.G. & Garrido, I. & Garrido, A.J., 2011. "Control-oriented Automatic System for Transport Analysis (ASTRA)-Matlab integration for Tokamaks," Energy, Elsevier, vol. 36(5), pages 2812-2819.
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    Cited by:

    1. Manuel De la Sen, 2019. "On the Design of Hyperstable Feedback Controllers for a Class of Parameterized Nonlinearities. Two Application Examples for Controlling Epidemic Models," IJERPH, MDPI, vol. 16(15), pages 1-23, July.
    2. Edorta Carrascal-Lekunberri & Izaskun Garrido & Bram Van der Heijde & Aitor J. Garrido & José María Sala & Lieve Helsen, 2017. "Energy Conservation in an Office Building Using an Enhanced Blind System Control," Energies, MDPI, vol. 10(2), pages 1-23, February.
    3. Aitor Marco & Aitor J. Garrido & Stefano Coda & Izaskun Garrido & TCV Team, 2019. "A Variable Structure Control Scheme Proposal for the Tokamak à Configuration Variable," Complexity, Hindawi, vol. 2019, pages 1-10, April.

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