Genetic Algorithm Optimized Linear Active Disturbance Rejection Control for Pressurizer pressure

Abstract Pressurizer control is an important part of the safe and economical operation of nuclear power plant. Due to its strong nonlinearity, time delay and complex internal physical process, it is difficult to design an appropriate pressurizer pressure control system. The pressurizer pressure control of pressurized water reactors adopts the traditional PID control, which leads to a large overshoot and long settling time. To achieve enhanced dynamic response characteristics, this study proposes a novel model-free linear active disturbance rejection control (MF-LADRC) architecture for pressurizer pressure regulation. By synergistically integrating a Genetic Algorithm (GA)-based parameter tuning framework with gain-scheduling strategies, the developed system dynamically optimizes controller parameters across diverse power operating conditions. The designed control system is implemented in the nonlinear dynamic model. Simulations are per-formed to verify the control performance. Comparisons are made with traditional control system. It is found that with LADRC pressurizer pressure control has smaller overshoot and settling time. The proposed control method is valuable for the control system design of pressurizer.