Main steam temperature control based on GA-BP optimised fuzzy neural network

Abstract

The high inertia and long time-delay characteristics of main steam temperature control system in a thermal power plant will reduce the system control performance. In order to solve this problem, a genetic algorithm-back propagation (GA-BP) optimised fuzzy neural network control strategy is proposed in this paper. Gauss function is chosen as membership function and fuzzy neural network is designed. GA combined with BP algorithm is chosen for the offline parameters optimisation of fuzzy neural network, and then BP algorithm is used for online parameters optimisation. GA-BP optimisation algorithm overcomes the shortcomings of GA algorithm or BP algorithm which is used to adjust the parameters of fuzzy neural network controller. The simulation experiment compared with cascade PID and fuzzy neural network is carried out. Simulation results show that the controller based on GA-BP optimised fuzzy neural network has faster response speed, smaller overshoot and error, better tracking performance, and reduces the lag effect of the control system under different load, working conditions and membership functions.