Main Steam Temperature Control System Based on CPSO and Neural Network

The main steam temperature object is the system with large inertia,large time delay,time-varying and nonlinear,and the traditional PID control cannot obtain satisfactory control performances.This paper proposed a control method based on CPSO and RBF neural network.PID parameters were tuned by RBF neural network,and the initial parameters of RBF neural network were optimized by chaos particle swarm optimization algorithm.The proposed control algorithm not only has adaptive ability and the characteristics of conventional PID control,but strengthens the adaptability of system.The simulation results show that the proposed control algorithm has better robustness and control quality,and strong ability of resisting disturbances.