PID controller design based on radial basis function neural networks for the steam generator level control

Abstract

Steam generator level control system is a vital control system for the Pressurized Water Reactor (PWR). However, the steam generator level process is a highly nonlinear and non-minimum phase system, the conventional Proportional- Integral-Derivative (PID) control scheme with fixed parameters was difficult to obtain satisfactory control performance. The Radial Basis Function (RBF) Neural Networks based PID control strategy (RBFNN-PID) is proposed for the steam generator level control. This method can identify the mathematical model of the steam generator via the RBF neural networks, and then the PID parameters can be optimized automatically to accommodate the characteristic variation of the process. The optimal number of the hidden layer neurons is also discussed in this paper. The simulation results shows that the PID controller designed based on the RBF neural networks has good control performance on the steam generator level control.