Reliable core density control with pellet fueling will be necessary to achieve required fusion power output in future fusion tokamaks such as ITER. The discrete nature of fuel pellets, however, complicates the density profile control problem significantly. As a solution, we propose a predictive density profile controller that considers fuel pellets as discrete actuators, while ensuring operation within prescribed density limits. The model predictive control (MPC) scheme we deploy combines the offset-free method to correct prediction model inaccuracies and our novel modified penalty term homotopy algorithm for real-time MPC (PTH-MPC). To demonstrate density profile control with discrete pellets, we couple the PTH-MPC density controller with JINTRAC integrated simulations of the ITER 15 MA/5.3 T scenario, using HPI2 to model discrete pellet ablation and deposition. We compare the density controller performance in integrated simulations using the Bohm/gyro-Bohm turbulent transport model against integrated simulations using the TGLF turbulent transport model. We highlight the necessity of treating pellets as discrete events for controller performance and for remaining within density limits. We conclude that PTH-MPC is a promising candidate for density profile control with pellets fueling in ITER and other future tokamaks and recommend further improvements using learning-based and robust MPC. We also note the limitations of quasi-linear turbulent transport models in simulations involving discrete pellets.
This paper proposes a predictive density profile controller that considers fuel pellets as discrete actuators to achieve reliable core density control in future fusion tokamaks like ITER. The model predictive control (MPC) scheme combines the offset-free method and a modified penalty term homotopy algorithm for real-time MPC. The controller is tested in integrated ITER simulations, highlighting the necessity of treating pellets as discrete events for controller performance and staying within density limits.