Backstepping Control of the Toroidal Plasma Current Profile in the DIII-D Tokamak

Boyer, Mark D.; Barton, Justin; Schuster, Eugenio; Walker, M.L.; Luce, T. C.; Ferron, J. R.; Penaflor, B.G.; Johnson, Robert D.; Humphreys, D.A.

doi:10.1109/tcst.2013.2296493

Cited by 40 publications

(31 citation statements)

References 41 publications

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“…a too high l (3) i ). This high l (3) i is mainly caused by the fact that the q(ρ = 0) in RAPTOR-observer becomes very small. This could be avoided in the future by using the sawtooth module implemented recently in RAPTOR [48] and incorporating real-time Thomson measurements instead of the line-averaged FIR and XTe measurements may further improve RAPTOR predictions.…”

Section: Comparison Of Online and Off-line Profile Reconstructionsmentioning

confidence: 99%

“…The current density profile of RAPTOR after 0.7 s is too peaked (i.e. a too high l (3) i ). This high l (3) i is mainly caused by the fact that the q(ρ = 0) in RAPTOR-observer becomes very small.…”

Section: Comparison Of Online and Off-line Profile Reconstructionsmentioning

confidence: 99%

“…The scalar l (3) i is a measure of the peakedness of the current density profile and is well constrained in LIUQE by magnetic measurements, contrarily to the q-profile itself. Note that l despite the underlying q-profiles being very different (as we have seen in figure 14).…”

Section: Comparison Of Online and Off-line Profile Reconstructionsmentioning

confidence: 99%

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Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

et al. 2017

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The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety factor profile (q-profile) and kinetic plasma parameters such as the plasma beta. This demands to establish reliable profile control routines in presently operational tokamaks. We present a model predictive profile controller that controls the q-profile and plasma beta using power requests to two clusters of gyrotrons and the plasma current request. The performance of the controller is analyzed in both simulation and TCV L-mode discharges where successful tracking of the estimated inverse q-profile as well as plasma beta is demonstrated under uncertain plasma conditions and the presence of disturbances. The controller exploits the knowledge of the time-varying actuator limits in the actuator input calculation itself such that fast transitions between targets are achieved without overshoot. A software environment is employed to prepare and test this and three other profile controllers in parallel in simulations and experiments on TCV. This set of tools includes the rapid plasma transport simulator RAPTOR and various algorithms to reconstruct the plasma equilibrium and plasma profiles by merging the available measurements with model-based predictions. In this work the estimated q-profile is merely based on RAPTOR model predictions due to the absence of internal current density measurements in TCV. These results encourage to further exploit model predictive profile control in experiments on TCV and other (future) tokamaks.

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Section: Comparison Of Online and Off-line Profile Reconstructionsmentioning

confidence: 99%

Section: Comparison Of Online and Off-line Profile Reconstructionsmentioning

confidence: 99%

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Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

et al. 2017

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“…ASTRA [30], CRONOS [43]) are not directly suitable for the task of real-time density reconstruction, since their execution time generally exceeds the discharge duration. It has been shown in [2][3][4][5]39,40] that low-complexity 1D models can be used for reconstruction and control of the temperature and safety factor profiles.…”

Section: Control-oriented 0+1d Model Of the Particle Transportmentioning

confidence: 99%

“…Plasma control has expanded in recent years from control of bulk plasma quantities (such as total plasma current, average particle density and average temperature) to control of the spatial distributions of these quantities, e.g. the profiles of temperature, safety factor and rotation [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Control-oriented modeling of the plasma particle density in tokamaks and application to real-time density profile reconstruction

Blanken

Felici

Rapson

et al. 2018

Fusion Engineering and Design

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Multi-experiment state-space identification of coupled magnetic and kinetic parameters in tokamak plasmas

Mavkov

Witrant

Prieur

et al. 2017

Control Engineering Practice

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International audienceThis paper describes an identification technique for control-oriented linear time-invariant models of the coupled dynamics of the electron temperature and the poloidal magnetic flux for advanced operational tokamak scenarios. The actuators consist of two neutral beam injectors, an electron cyclotron current drive and the ohmic coil that provides the loop voltage at the plasma surface. The model is identified using a combination of subspace and output-error methods for state-space multiple-input and multiple-output system identification. This identification is applied on sets of simulated data from the METIS tokamak simulator with parameters typical of the DIII-D tokamak, and the results of the identification are presented

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Backstepping Control of the Toroidal Plasma Current Profile in the DIII-D Tokamak

Cited by 40 publications

References 41 publications

Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

Control-oriented modeling of the plasma particle density in tokamaks and application to real-time density profile reconstruction

Multi-experiment state-space identification of coupled magnetic and kinetic parameters in tokamak plasmas

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