Enhancing deep reinforcement learning with integral action to control tokamak safety factor

Abstract

Recent advances in the use of Artificial Intelligence to control complex systems make it suitable for profile plasma control. In this work, we propose an algorithm based on Deep Reinforcement Learning to control the safety factor profile with a feedback design. For this purpose, we first derive a device-specific control-oriented model with fast simulation time. Then, in order to enhance robustness with respect to external disturbances and model errors, we include an error time integrator into the controller. A cascade of the kinetic and magnetic models with the error time integrator is used in the learning procedure of the feedback controller. Finally, to illustrate the efficiency of the proposed design procedure, the obtained controller is tested in a reference plasma simulator, the Raptor simulator.