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Theses

Data-driven modelling and control of plasma current profile and kinetic parameters in advanced tokamak scenarios

Abstract : This thesis investigates data-driven modelling and control of plasma current profile and kinetic parameters in advanced tokamak scenarios. The nonlinear data-driven modelling approach is, for the first time, proposed in this thesis to incorporate the nonlinear dynamics in advanced tokamak plasmas, for instance, wave-plasma interaction and bootstrap effects. With the linear data-driven models, a number of advanced control approaches are explored, $mathcal{H}_infty$ control, linear-quadratic-integral control, the internal model control and model predictive control. Both experiments and numerical simulations have been performed to validate the effectiveness of the alternative control approaches. To broaden the control region, adaptive control approaches are proposed, including model-free extremum-seeking control and model reference adaptive control. These new control algorithms have been implemented and evaluated via nonlinear closed-loop METIS simulations on the EAST and ITER tokamaks.
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https://tel.archives-ouvertes.fr/tel-03276054
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Submitted on : Thursday, July 1, 2021 - 4:05:09 PM
Last modification on : Friday, March 25, 2022 - 9:40:54 AM
Long-term archiving on: : Saturday, October 2, 2021 - 7:05:33 PM

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  • HAL Id : tel-03276054, version 1

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Sen Wang. Data-driven modelling and control of plasma current profile and kinetic parameters in advanced tokamak scenarios. Automatic. Université Grenoble Alpes [2020-..], 2021. English. ⟨NNT : 2021GRALT013⟩. ⟨tel-03276054⟩

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