Adaptive neural network sliding mode controller design for load following of nuclear power plant

The power control and load following of nuclear reactors have always been a research topic of concern in the field of nuclear power. In order to improve the control system performance and load following performance, this paper proposes a sliding mode control method based on adaptive RBF neural network (RBFNN) for pressurized water reactor (PWR) power control and load following. Firstly, based on the operation mechanism and energy transport process of PWR, the PWR power model is established based on the point-reactor equation. Then, based on Lyapunov stability theory, an ideal sliding mode controller is designed for a general nonlinear second-order model. Secondly, considering that some state variables in the actual PWR model cannot be directly measured, which leads to the problem that the actual control law cannot be directly applied, RBFNN is used as the controller, and its output is used to replace the actual control law and approximate the ideal control law. Adaptive algorithm is adopted to improve the approximation effect of RBFNN. Finally, the simulation results show that the proposed control method can achieve the PWR load following task well under the set working conditions, and has good robustness to disturbances. Compared with the traditional sliding mode control (TSMC) method, this method can effectively eliminate chattering phenomenon and greatly improve the control performance of the sliding mode controller.