Iterative Learning Optimisation and Control of MAST-U Breakdown and Early Ramp-up Scenarios

Plasma initiation is an important phase in a tokamak discharge and its design and optimization is getting more and more attention in view of the operation of large tokamaks like ITER. The main objective of magnetic control during this phase is to obtain a high electric field to ionize the neutral particles with a low stray magnetic field to avoid the ionized particles escaping towards the chamber walls, in a sufficiently large region inside the vacuum chamber, and then, sustain the plasma current rise whilst maintaining the force balance equilibrium. This paper describes the application of a recent plasma initiation optimisation algorithm, implemented in the CREATE-BD code, to the MAST Upgrade (MAST-U) tokamak. The procedure is based on quadratic programming and iterative learning control methodologies. In fact the breakdown scenario is corrected step by step on the basis of the previous experiments converging to an optimal solution in few steps.