MAIN STEAM TEMPERATURE CONTROL USING RBF NEURAL NETWORK BASED ON HYBRID LEARNING ALGORITHM

WANG Jie; JIANG Guo-qiang; WANG ShuanCollege of Electrical Engineering

doi:10.19666/j.rlfd.2009.02.008

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MAIN STEAM TEMPERATURE CONTROL USING RBF NEURAL NETWORK BASED ON HYBRID LEARNING ALGORITHM

更新时间：2025-09-28

- MAIN STEAM TEMPERATURE CONTROL USING RBF NEURAL NETWORK BASED ON HYBRID LEARNING ALGORITHM
- Thermal Power Generation Vol. 38, Issue 2, Pages: 28-31(2009)
- 作者机构：
  
  郑州大学电气工程学院
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.2009.02.008
  CLC： TP183
- Published：2009
- 稿件说明：
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WANG Jie, JIANG Guo-qiang, WANG ShuanCollege of Electrical Engineering. MAIN STEAM TEMPERATURE CONTROL USING RBF NEURAL NETWORK BASED ON HYBRID LEARNING ALGORITHM[J]. Thermal power generation, 2009, 38(2): 28-31.
DOI：

WANG Jie, JIANG Guo-qiang, WANG ShuanCollege of Electrical Engineering. MAIN STEAM TEMPERATURE CONTROL USING RBF NEURAL NETWORK BASED ON HYBRID LEARNING ALGORITHM[J]. Thermal power generation, 2009, 38(2): 28-31. DOI： 10.19666/j.rlfd.2009.02.008.

摘要

针对火电厂主蒸汽温度的大迟延、模型不确定性特点

提出一种使用径向基(RBF)神经网络整定PID串级主蒸汽温度控制策略。采用一种最近邻聚类法和梯度下降法相结合的混合学习算法构造RBF神经网络

在线辨识被控对象并对PID主控制器参数进行在线调整。仿真结果表明

基于混合学习算法的RBF神经网络PID控制器具有控制精度高、响应速度快的优点

系统动态品质优于常规算法的RBF神经网络PID控制。

Abstract

Directing against the features of long time delay and uncertainty in the main steam temperature control of thermal power plants

a RBF-PID cascade control strategy has been put forward.Adopting a hybrid learnging algorithm

which combines the nearest neighbor-clustering method with the gradient descent method

to construct a RBF neural network

the controlled objects can be identified on-line and the parameters of PID master controller can be adjusted on-line.The simulation results show that the PID controller using RBF neural network based on hybrid learning algorithm has the advantages of high precision control and rapid response speed

the dynamic quality of system being more better than that of PID controller using RBF neural network of conventional learning algorithm.

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