Neural network model predictive control of core power of Qinshan nuclear power plant based on reinforcement learning

The power change of Pressurized water reactors (PWRs) is a highly complex nonlinear coupling process, which requires the power controller to have a high nonlinear control capability. Since the traditional model predictive control (MPC) algorithm has the problems of insufficient robustness and adaptivity, this study proposes a neural network-based neural network model predictive control method based on model predictive control and optimizes its control parameters in real time by using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The designed control method is validated on the simulation model of the Qinshan nuclear power plant (NPP). Simulation results show that the proposed control method can realize effective control of power under a variety of power changing conditions and dynamic adjustment of control parameters.