Safety Factor Profile Regulation via Self-Triggered Model Predictive Control in the EAST Tokamak

The tokamak, a viable option for harnessing nuclear fusion energy, employs strong helical magnetic fields to confine a plasma (ionized gas) within a toroidal vacuum chamber. Optimal performance in tokamaks necessitates sophisticated control mechanisms to shape the spatial profiles of specific plasma properties. One such property is the safety factor
$q$
, which measures the pitch of the helical magnetic field lines. The dynamics of the
$q$
profile in tokamaks depends on the gradient of the poloidal magnetic flux, which is governed by a nonlinear partial differential equation referred to as the magnetic diffusion equation. In this work, model predictive control (MPC) is proposed to regulate the
$q$
profile in the EAST tokamak. The finite-horizon optimal control problem (FHOCP) associated with the MPC approach is defined with the goal of minimizing the tracking error between observed and target gradients of the poloidal magnetic flux while satisfying input and state constraints. To address the optimization problem in real time, a simplified model is derived from the magnetic diffusion equation. As a difference from previous efforts in this area, a self-triggered mechanism is implemented within the MPC algorithm to prevent redundant computations arising in fixed sampling-time MPC schemes. Simulation studies show that the proposed controller has the capability of regulating the
$q$
profile through the manipulation of the plasma current and the heating and current-drive powers. A comparison with regular fixed-sampling-time MPC methods demonstrates that the proposed self-triggered MPC strategy optimizes performance by avoiding redundant computations and saving computational time.