Delay Separated Neural Network Inverse Control in Main-Steam Temperature System

Abstract

In order to improve the control effect of the main steam temperature with large time delay, this paper proposed a delay separated neural network inverse (DSNNI) control scheme. The delay time and the positive model without delay were given by using adaptive linear element and BP network. The neural network inverse model of the positive model without delay was built   on that basis. An appropriate reference model was selected to make the inverse model’s output smoothing. It is an open-loop control system when the model is cascaded into original system. It will avoid the instability caused by the closed-loop control systems. Off-line identification and on-line identification are combined to get the inverse model in order to reduce the steady-state error and make the system have fine adaptive capacity. Detail simulation tests are carried out on the given 300MW power unit. Tests show that the neural network inverse control with delay time separation can get rapid and smooth output for the main steam temperature system. It is able to overcome the adverse effects caused by the time delay and the parameters changes. Compared with the cascade PID controller, the adjustment time of DSNNI reduces from 600s to380s and shows faster response, better robustness and anti-interference performance.