Automated shot-to-shot optimization of the plasma start-up scenario in the TCV tokamak

Plasma start-up concerns the initial formation of a tokamak plasma by obtaining breakdown at a desired time and location, burnthrough of impurities until a fully ionized plasma is obtained, while ramping up the plasma current following a desired trajectory and maintaining the plasma at the desired location in the vacuum vessel. This is typically achieved manipulating poloidal magnetic fields, gas injection and possibly auxiliary heating. Model-based design techniques for plasma start-up have been gaining increasing attention in view of future large tokamaks which have more stringent constraints and less room for trial-and-error. In this paper, we formulate the tokamak start-up scenario design problem as a constrained optimization problem using a relatively simple electromagnetic model of the formation phase, neglecting details of the breakdown and burnthrough physics. We also introduce a novel shot-to-shot correction algorithm, based on the Iterative Learning Control concept, to compensate for unavoidable modeling errors based on experimental data. The effectiveness of the approach is demonstrated in experiments on the TCV tokamak showing that the target ramp-up scenario could be obtained in a small number of shots.