摘要
锅炉的主汽温被控对象是一个大惯性、大迟延、非线性且对象变化的系统,常用的汽温控制系统有串级PID控制和基于BP神经网络的PID控制。串级PID控制一般能将主汽温控制在允许的范围内;基于BP神经网络的PID控制将神经网络所具有的自学习能力与PID控制器的鲁棒性相结合,能实现对非线性、大时滞系统模型的控制。对这两种策略在不同负荷下进行了实验仿真,该结果对当前电厂的经济性和安全性有一定参考价值。
Boiler main steam temperature controlled object is a large inertia, large time delay, nonlinear and the changeable object system. Cascade PID control and PID control based on BP neural network are commonly used in the steam temperature control system. Cascade PID control can control main steam temperature in the allowed range, PID control based on BP neural network , which combines the ability of self-study of the neural network with the robustness of the PID controller can realize the control to the nonlinear, large delay system model. This article analyses the two strategies in different loading conditions, the results have a certain n-ference value for the current power plant in economy and safety.
出处
《微型机与应用》
2011年第24期76-78,共3页
Microcomputer & Its Applications
关键词
BP神经网络
PID控制
主汽温
BP neural network
PID control
main steam temperature
