电厂过热汽温神经PID控制系统的仿真研究

程启明; 陈刚; 王勇浩

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上海电力大学学报:2005,21(1):37-41

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电厂过热汽温神经PID控制系统的仿真研究

程启明¹, 陈刚², 王勇浩³

(1.上海电力学院电力与自动化工程学院, 上海, 200090;2.上海久隆电力集团科技公司, 上海, 200082;3.上海理工大学光电学院, 上海, 200093)

A Simulation Research on the Power Station Superheated Steam Temperature Neural Network PID Control System

CHENG Qi-ming¹, CHEN Gang², WANG Yong-hao³

(1.School of Electric Power and Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China;2.Shanghai Jiulong Electric Power Development Co. Ltd., Shanghai 200082, China;3.School of Light Electricity, Shanghai Science and Engineering University, Shanghai 200093, China)

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本文已被：浏览 781次下载 559次
投稿时间：2004-11-24

中文摘要: 针对火电厂主汽温被控对象的大迟延、模型不确定性,设计了基于RBF神经网络的神经PID汽温控制系统,并对控制系统进行仿真试验,其仿真结果表明了设计的控制器具有良好的位置跟随性、抗干扰性和鲁棒性.

中文关键词: 神经网络过热汽温控制 MATLAB仿真 PID控制

Abstract:In order to overcome the large delay and the uncertainty of the main-stream temperature object in fossil-fired power station, a neural PID temperature control system based on RBF neural network is proposed. It contains two neural netwoks: system-online identifier and neural PID controller. The simulation results show that the designed system has good position tracking propery, adaptability to overcome disturbances and robustness.

keywords: neural network superheated steam temperature MATLAB simulation PID control

文章编号：20050109 中图分类号： 文献标志码：

基金项目:上海市教委发展基金(020K02);江苏省计算机信息处理技术重点实验室开放课题基金资助(苏大科[2000]07号).

作者	单位
程启明	上海电力学院电力与自动化工程学院, 上海, 200090
陈刚	上海久隆电力集团科技公司, 上海, 200082
王勇浩	上海理工大学光电学院, 上海, 200093

Author Name	Affiliation
CHENG Qi-ming	School of Electric Power and Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
CHEN Gang	Shanghai Jiulong Electric Power Development Co. Ltd., Shanghai 200082, China
WANG Yong-hao	School of Light Electricity, Shanghai Science and Engineering University, Shanghai 200093, China

引用文本：
程启明,陈刚,王勇浩.电厂过热汽温神经PID控制系统的仿真研究[J].上海电力大学学报,2005,21(1):37-41.
CHENG Qi-ming,CHEN Gang,WANG Yong-hao.A Simulation Research on the Power Station Superheated Steam Temperature Neural Network PID Control System[J].Journal of Shanghai University of Electric Power,2005,21(1):37-41.