Main Stream Temperature Control System Based on Neural Network

In order to overcome the large delay and the uncertainty of the main-stream temperature object in fossil-fired power station, a control system based on neural network is proposed. The inner loop uses a general proportion adjuster and the outer loop uses a single neuron PID controller with identification implement. The identification implement is a three layer BP network based on δ-rule. Hebb study arithmetic is adopted. The system desired value and the controller output constitute the teacher's signals. Through simulation in various situations, it is validated that the control quality and the robustuess of this control system apparently are superiors to the general PID system.