Actuator Allocation with Adaptive Estimation of Time-Varying Uncertain Parameters for Nonlinear Burn Control

The ITER tokamak will produce energy through the nuclear fusion that can occur when an ionized gas, or plasma, is brought to extreme temperatures. To drive the plasma to regimes that allow net-positive power output, burn control algorithms regulate the plasma's temperature and density by requesting external heating and fueling. These control requests can be satisfied with various actuators. In this work, a model-based, nonlinear burn control algorithm is synthesized along with an optimal actuator allocation algorithm that man-ages ITER's actuators. This actuator allocation algorithm con-siders actuator delays and various other complex phenomena, such as the fueling contribution from neutral beam injection (NBI), when searching for the optimal mapping between the burn control algorithm's requests and the available actuators. The burn control and actuator allocation algorithms are based on control-oriented models for the plasma and actuators that in-clude uncertainty. Uncertainty is modeled by asserting that the value of specific parameters are not known to the algorithms. In the model of the plasma system, these uncertain parameters describe the quality of the plasma confinement and plasma-wall effects such as impurity sputtering. In the models for the actuator systems, there are state-dependent, time-varying uncertainties such as the NBI thermalization delay, the uneven NBI power deposition between ions and electrons, and the pellet fueling efficiency. Both the burn control and actuator allocation algorithms are designed to handle these uncertain parameters through adaptive estimation. A simulation study illustrates the capability of these algorithms working together.