Adaptive Predictive Control for Core Power Based on CPSO Rolling Optimization

In view of the nonlinear and reactive constraint of nuclear reactors in the process of variable power, this paper proposes an improved generalized predictive control (JGPC) for core power control. The JGPC calculates the predicted output value by predicting the model parameters and recursive relationships. At the same time, chaos particle swarm optimization (CPSO), which is improved by the sinusoidal chaos strategy and nonlinear inertia weight, is applied to the rolling optimization of JGPC. In the process of optimization, the reactive constraint are dealt with by setting optimization boundary and chaos strategy. The controlled auto-regressive integral moving average (CARIMA) model of core power is established as the JGPC prediction model, and the forgetting factor recursive least squares (FFRLS) method is used to identify the model parameters online. The JGPC controller is simulated and validated based on MATLAB platform. The results show that the controller can make the core power follow the set value quickly and steadily under the condition of satisfying the constraint, and has a certain anti-interference ability.