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

Blanken, T. C.; Felici, F.; Rapson, C.; Baar, M.R. de; Heemels, W.P.M.H.

doi:10.1016/j.fusengdes.2017.11.006

Cited by 30 publications

(49 citation statements)

References 56 publications

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“…This model was originally developed to improve the density control in TCV and ASDEX Upgrade [12]. For a detailed description of the model, including the model equations, we refer the reader to [12]. For the work presented in this paper, the model was updated to capture elements relevant specifically for ramp-up.…”

Section: Description Of the Control-oriented Modelmentioning

confidence: 99%

“…The vacuum and wall are regarded as zero dimensional, but the plasma is described by a 1D partial differential equation for the electron density profile. The details on how these processes are modelled can be found in [12]. A summary of the parameter values is given in the appendix.…”

Section: Inventory Descriptionmentioning

confidence: 99%

“…To test our controller, we use a control-oriented model of the plasma particle density evolution [12], which has been specially updated to include issues that govern the particle density evolution in ITER during ramp-up.…”

Section: Introductionmentioning

confidence: 99%

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Density control in ITER: an iterative learning control and robust control approach

et al. 2017

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Plasma density control for next generation tokamaks, such as ITER, is challenging because of multiple reasons. The response of the usual gas valve actuators in future, larger fusion devices, might be too slow for feedback control. Both pellet fuelling and the use of feedforward-based control may help to solve this problem. Also, tight density limits arise during ramp-up, due to operational limits related to divertor detachment and radiative collapses. As the number of shots available for controller tuning will be limited in ITER, in this paper, iterative learning control (ILC) is proposed to determine optimal feedforward actuator inputs based on tracking errors, obtained in previous shots. This control method can take the actuator and density limits into account and can deal with large actuator delays. However, a purely feedforward-based density control may not be sufficient due to the presence of disturbances and shot-to-shot differences. Therefore, robust control synthesis is used to construct a robustly stabilizing feedback controller. In simulations, it is shown that this combined controller strategy is able to achieve good tracking performance in the presence of shot-to-shot differences, tight constraints, and model mismatches.

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Section: Description Of the Control-oriented Modelmentioning

confidence: 99%

Section: Inventory Descriptionmentioning

confidence: 99%

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Density control in ITER: an iterative learning control and robust control approach

et al. 2017

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“…The real-time density profile estimation algorithm chosen employs an interpretative model for the density in a dynamic state estimator, integrating the predicted density evolution by taking into account all real-time available measurements [9,16]. The observer estimates the density iteratively by solving one-sample ahead model-based predictions from the previous estimate and updating the predictions by using the measurement residuals.…”

Section: Refined Feedback Control With Model-based Density Profile Obmentioning

confidence: 99%

“…Since the dynamic state observer was aware of the DCN interferometer signal degradation due to fringe jumps (see figure 4), it used only the bremsstrahlung measurements in the pellet phase. The applied model, named RAPDENS, in the dynamic state observer [16], can be fine-tuned to improve the profile estimates.…”

Section: Refined Feedback Control With Model-based Density Profile Obmentioning

confidence: 99%

Feedback controlled, reactor relevant, high-density, high-confinement scenarios at ASDEX Upgrade

et al. 2018

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One main programme topic at the ASDEX Upgrade all-metal-wall tokamak is development of a high-density regime with central densities at reactor grade level while retaining high-confinement properties. This required development of appropriate control techniques capable of coping with the pellet tool, a powerful means of fuelling but one which presented challenges to the control system for handling of related perturbations. Real-time density profile control was demonstrated, raising the core density well above the Greenwald density while retaining the edge density in order to avoid confinement losses. Recently, a new model-based approach was implemented that allows direct control of the central density. Investigations focussed first on the N-seeding scenario owing to its proven potential to yield confinement enhancements. Combining pellets and N seeding was found to improve the divertor buffering further and enhance the operational range accessible. For core densities up to about the Greenwald density, a clear improvement with respect to the non-seeding reference was achieved; however, at higher densities this benefit is reduced. This behaviour is attributed to recurrence of an outward shift of the edge density profile, resulting in a reduced peeling-ballooning stability. This is similar to the shift seen during strong gas puffing, which is required to prevent impurity influx in ASDEX Upgrade. First tests indicate that highly-shaped plasma configurations like the ITER base-line scenario, respond very well to pellet injection, showing efficient fuelling with no measurable impact on the edge density profile.

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Real‐time estimation of the electron temperature profile in DIII‐D by leveraging neural‐network surrogate models

Morosohk

Schuster

2023

Contributions to Plasma Physics

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Control of both the magnitude and the shape of tokamak profiles will be necessary to achieve stable, high‐performance plasmas. In order to reject disturbances in real time, feedback‐control algorithms rely on accurate real‐time knowledge of the plasma state. When diagnostics alone are insufficient, because they are limited in number or their measurements are too noisy, observers can be used to combine diagnostic data with a response model to provide a better estimation of different plasma properties. An observer has been developed to estimate the electron temperature profile in real time using both diagnostic data from the Thomson scattering system and a model based on the electron heat transport equation describing the evolution of the electron temperature profile. Neural network surrogate models are leveraged to help improve the overall model prediction while staying within computation time constraints for real‐time use. The observer algorithm is shown in offline tests to produce smooth profiles that are consistent with both the diagnostic data and the electron heat transport equation. When implemented into the real‐time plasma control system, this observer will provide valuable information on the electron temperature profile to many potential feedback‐control applications.

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

Cited by 30 publications

References 56 publications

Density control in ITER: an iterative learning control and robust control approach

Density control in ITER: an iterative learning control and robust control approach

Feedback controlled, reactor relevant, high-density, high-confinement scenarios at ASDEX Upgrade

Real‐time estimation of the electron temperature profile in DIII‐D by leveraging neural‐network surrogate models

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