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.

This note describes a method to train a Neural Network so that it approximates a control Lyapunov function for a nonlinear system in affine form. The network is trained in a physics-informed fashion, as the training data are generated by enforcing the negativity of the orbital derivative of the clf along the system trajectories in a large set of collocation points. Positive-definiteness of the clf is guaranteed by the choice of the network structure. The network is then used to derive a stabilizing control law based on the well-known Sontag’s formula. The validity of the proposed approach is illustrated through numerical examples.

This note describes a method to train a Neural Network so that it approximates a control Lyapunov function for a nonlinear system in affine form. The network is trained in a physics-informed fashion, as the training data are generated by enforcing the negativity of the orbital derivative of the clf along the system trajectories in a large set of collocation points. Positive-definiteness of the clf is guaranteed by the choice of the network structure. The network is then used to derive a stabilizing control law based on the well-known Sontag’s formula. The validity of the proposed approach is illustrated through numerical examples.

This work addresses the input current allocation problem for the magnetic control system of the Tokamak à Configuration Variable (TCV). Given the available actuators (i.e. poloidal field coils) on the TCV device, this work aims to optimize the input current configuration concerning a desired cost function, minimizing the effect on the controlled output, which is, in this case, considered as the plasma shape. This request is formulated as a dynamic control allocation problem, and a solution is proposed, discussed, and validated by numerical simulations on TCV models and data.