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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.
Highlights
The generation experimental tokamak, ITER, is currently being built
The model from [12] was adapted for use in ITER, and it was shown that the model can reproduce the density evolution in the reference ramp-up scenario from DINA simulations
It has been shown that simple proportional feedback control lacks performance for the ITER ramp-up scenarios
Summary
The generation experimental tokamak, ITER, is currently being built. This device is larger than its predecessors [1], which gives rise to multiple challenges, one of which is plasma density control. The topic of this paper is the control of the particle density in ITER despite the issues mentioned above This controller must be able to deal with the large time scale separation in actuators (pellets and gas), fast transitions and changes in dynamics of the plasma during the plasma ramp-up, and complications in the modelling of plasma fuelling. We propose to use iterative learning control (ILC), a control method whereby the time trajectory of the actuator input signals is modified from preceding experiments (all having the same density reference) in such a way that the norm of the tracking error over the period of interest is reduced [6] This can be achieved by using the result of one trial to design an improved feedforward signal for the next.
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