[1]曹奔,袁忠于,刘洪.基于粒子群算法的烧结炉系统辨识及神经网络控制[J].郑州大学学报(工学版),2017,38(05):39-43.[doi:10.13705/j.issn.1671-6833.2017.02.022]
　Cao Ben,Yuan Zhong,Yu Liu Hong.Sintering Furnace System Identification Based on Particle Swarm Algorithm and Neural Network Control[J].Journal of Zhengzhou University (Engineering Science),2017,38(05):39-43.[doi:10.13705/j.issn.1671-6833.2017.02.022]
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基于粒子群算法的烧结炉系统辨识及神经网络控制()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
38
期数:
2017年05期
页码:
39-43
栏目:
出版日期:
2017-09-26

文章信息/Info

Title:
Sintering Furnace System Identification Based on Particle Swarm Algorithm and Neural Network Control
作者:
曹奔; 袁忠于; 刘洪
兰州交通大学机电工程学院,甘肃兰州,730070
Author(s):
Cao Ben;  Yuan Zhong;  Yu Liu Hong
School of Mechanical and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu, 730070
关键词:
粒子群算法; 系统辨识; 神经网络监督控制; PID控制
Keywords:
DOI:
10.13705/j.issn.1671-6833.2017.02.022
文献标志码:
A
摘要:
烧结炉在加热过程中,模型参数易发生变化,而传统的PID控制很难达到理想的控制效果.本文运用粒子群优化算法辨识烧结炉的数学模型,针对烧结炉惯性大、时变、大滞后等特点,采用基于RBF神经网络的监督控制,将PID控制与神将网络控制相结合.当温度或模型参数发生较大变化时,PID控制起主要作用,神经网络起调节作用,补偿PID控制的不足.MATLAB软件仿真结果说明,该方法能够提高烧结炉的控制精度,具有一定的实用性.
Abstract:
During heating process of sintering furnace,the model parameters were easy to change,and traditional PID control was difficult to achieve the desired control effect.This paper used particle swarm optimization algorithm to identify the mathematical model of sintering furnace,for sintering furnace with high inertia,time-variation and strong time delay etc,a method of supervision and control based on RBF neural network,which combined PID control with neural network control.When temperature or parameters changed greatly,PID control played a major role.neural network played a regulatory role and compensated the shortage of PID control.The simulation results of MATLAB software showed that this method could improve the control precision of sintering furnace,which had a certain practicality.

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