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

T. Ravensbergen, P.C. de Vries, F. Felici, T.C. Blanken, R. Nouailletas, L. Zabeo

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
94 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number016048
JournalNuclear Fusion
Volume58
Issue number1
DOIs
Publication statusPublished - 1 Jan 2018

Fingerprint

approach control
learning
shot
actuators
controllers
gas valves
refueling
ramps
feedback control
detachment
pellets
plasma density
disturbances
fusion
tuning
synthesis
simulation

Keywords

  • feedforward control
  • gas fuelling
  • ITER
  • pellet fuelling
  • plasma density control
  • ramp-up
  • robust control

Cite this

Ravensbergen, T., de Vries, P. C., Felici, F., Blanken, T. C., Nouailletas, R., & Zabeo, L. (2018). Density control in ITER: an iterative learning control and robust control approach. Nuclear Fusion, 58(1), [016048]. https://doi.org/10.1088/1741-4326/aa95ce
Ravensbergen, T. ; de Vries, P.C. ; Felici, F. ; Blanken, T.C. ; Nouailletas, R. ; Zabeo, L. / Density control in ITER : an iterative learning control and robust control approach. In: Nuclear Fusion. 2018 ; Vol. 58, No. 1.
@article{702d50443f5b408eaf7ec8428312ed50,
title = "Density control in ITER: an iterative learning control and robust control approach",
abstract = "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.",
keywords = "feedforward control, gas fuelling, ITER, pellet fuelling, plasma density control, ramp-up, robust control",
author = "T. Ravensbergen and {de Vries}, P.C. and F. Felici and T.C. Blanken and R. Nouailletas and L. Zabeo",
year = "2018",
month = "1",
day = "1",
doi = "10.1088/1741-4326/aa95ce",
language = "English",
volume = "58",
journal = "Nuclear Fusion",
issn = "0029-5515",
publisher = "Institute of Physics",
number = "1",

}

Ravensbergen, T, de Vries, PC, Felici, F, Blanken, TC, Nouailletas, R & Zabeo, L 2018, 'Density control in ITER: an iterative learning control and robust control approach', Nuclear Fusion, vol. 58, no. 1, 016048. https://doi.org/10.1088/1741-4326/aa95ce

Density control in ITER : an iterative learning control and robust control approach. / Ravensbergen, T.; de Vries, P.C.; Felici, F.; Blanken, T.C.; Nouailletas, R.; Zabeo, L.

In: Nuclear Fusion, Vol. 58, No. 1, 016048, 01.01.2018.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Density control in ITER

T2 - an iterative learning control and robust control approach

AU - Ravensbergen, T.

AU - de Vries, P.C.

AU - Felici, F.

AU - Blanken, T.C.

AU - Nouailletas, R.

AU - Zabeo, L.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

KW - feedforward control

KW - gas fuelling

KW - ITER

KW - pellet fuelling

KW - plasma density control

KW - ramp-up

KW - robust control

UR - http://www.scopus.com/inward/record.url?scp=85038862974&partnerID=8YFLogxK

U2 - 10.1088/1741-4326/aa95ce

DO - 10.1088/1741-4326/aa95ce

M3 - Article

AN - SCOPUS:85038862974

VL - 58

JO - Nuclear Fusion

JF - Nuclear Fusion

SN - 0029-5515

IS - 1

M1 - 016048

ER -