|本期目录/Table of Contents|

[1]鲁 博,张寿明,冯欣怡,等.单神经元PID控制仿真与实现研究[J].工业仪表与自动化装置,2024,(01):3-8.[doi:DOI:10.19950/j.cnki.CN61-1121/TH.2024.01.001]
 LU Bo,ZHANG Shouming,FENG Xinyi,et al.Research on simulation and implementation of single neuron PID control[J].Industrial Instrumentation & Automation,2024,(01):3-8.[doi:DOI:10.19950/j.cnki.CN61-1121/TH.2024.01.001]
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单神经元PID控制仿真与实现研究(PDF)

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2024年01期
页码:
3-8
栏目:
出版日期:
2024-02-15

文章信息/Info

Title:
Research on simulation and implementation of single neuron PID control
文章编号:
1000-0682(2024)01-0003-06
作者:
鲁 博1张寿明2冯欣怡3郭英英1
(1.陕西科技大学镐京学院 咸阳市人工智能重点实验室,陕西 西安 712046;2.昆明理工大学 信息工程与自动化学院,云南 昆明 650093;3.西安工商学院 计算机学院,陕西 西安 710032)
Author(s):
LU Bo1 ZHANG Shouming2 FENG Xinyi3 GUO Yingying1
(1.Haojing College, Shaanxi University of Science and Technology, Shannxi Xi’an 712000, China; 2.Kunming University of Science and Technology, Yunnan Kunming 650093, China; 3.Xi’an Institute of Technology, Shannxi Xi’an 710032, China)
关键词:
PID控制神经网络智能低成本
Keywords:
PID controlneural networkintelligencelow cost
分类号:
TP27
DOI:
DOI:10.19950/j.cnki.CN61-1121/TH.2024.01.001
文献标志码:
A
摘要:
经典PID控制参数整定方法繁琐并且容易对设备造成损伤,此外,当系统对象存在非线性、环境变化或者其他因素时,系统的稳定性和响应速度也会受到一定的影响。为了解决这些问题,该文引入神经网络与PID控制方法相结合,控制对象选择双容水箱,对系统进行多次仿真、对比,然后提出神经网络控制仿真参数设置方法,进而将神经网络PID控制算法应用于实际控制系统中,实验证明该创新方案不仅解决了PID控制方法存在的问题,还找到了一个低成本的升级方案,为控制系统提供了重要的技术支持
Abstract:
The classical PID control parameter tuning method is cumbersome and easy to cause damage to the equipment. In addition, when the system object has nonlinear, environmental changes or other factors, the stability and response speed of the system will be affected to some extent.In order to solve these problems, this paper introduces the combination of neural network and PID control method, the control object is selected two-capacity water tank, the system is simulated and compared several times, and then the neural network control simulation parameter setting method is proposed, and then the neural network PID control algorithm is applied to the actual control system.The experiment proves that this innovative scheme not only solves the problems of PID control method, but also finds a low-cost upgrade scheme, which provides important technical support for the control system.

参考文献/References:

[1]吴宏鑫.工程实际中的控制理论和方法的研究与展望[J].控制理论与应用,2014,31(12):1627-1631.

[2]柴天佑.工业过程控制系统研究现状与发展方向[J].中国科学,2016,46(8):1013-1014.
[3]吴宏鑫,常亚菲.智能控制系统简述及研究构想[J].空间技术与应用,2019,45(4):1-6.
[4]袁世通.基于神经网络的非线性预测控制现状与发展[C].年燃煤发电清洁燃烧与污染物综合治理技术研讨会暨环保技术与装备专委会年会论文集.上海:知网数字出版技术股份有限公司,2016:66-72.
[5]丁锋,徐玲,刘喜梅.传递函数辨识(1)阶跃响应两点法和三点法[J].青岛科技大学学报,2018,39(1):1-14.
[6]林巍,王亚刚.串级控制系统闭环辨识及PID参数整定[J].控制工程,2018,25(1):11-18.
[7]王志新,谷云东,王加银,等.双容水箱上的几种液位控制实验及被控对象的数学模型[J].北京师范大学学报(自然科学版),2006,42(2):126-130.
[8]王志新,谷云东.随机出入水双容水箱液位控制实验及被控对象的数学模型[J].化工自动化仪表,2006,33(2):13-16.
[9]张晓莉,杜文玉,王毅漩,等.基于PLC系统的双容水箱液位控制[J].微型机与应用,2017,36(17):1-4.
[10]汪玉凤,王鑫.基于组态王网络OPC的数据监控系统[J].仪表技术与传感技术,2010(8):52-53.
[11]石建国,杨磊.基于组态王的数据采集通信与控制系统设计[J].菏泽学院学报,2018,40(5):11-16
[12]PATIL Sanjay R, AGASHE Sudhir D.Auto tuned PID and neural network predictive controller for a flow loop pilot plant[J].Proceedings.?Volume 72 , Issue P3?. 2023,754-760.
[13]YANG Taoqing,ZHENG Xia,XIAO Hongwei,et al.Drying Temperature Precision Control System Based on Improved Neural Network PID Controller and Variable-Temperature Drying Experiment of Cantaloupe Slices[J].Plants (Basel, Switzerland).?Volume 12 , Issue 12?. 2023,2275-2295.
[14]JEYARAJj Pandia Rajan.Nadar Edward Rajan Samuel,Real‐time data‐driven PID controller for multivariable process employing deep neural network?[J]?Asian Journal of Control.?Volume 24 , Issue 6?. 2022,3240-3251.
[15]刘雪飞,苏师师,蒯亮,等.基于新型P1D算法的整流罩空调控制系统研[J].电子技术应用,2021,47 (12):74-78.

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 CHEN Hongxi,ZOU Yimin.Research on application of real-time communication of MATLAB with S7-200 PLC in process control experimental device based on OPC[J].Industrial Instrumentation & Automation,2015,(01):55.

备注/Memo

备注/Memo:
收稿日期:2023-08-12基金项目:陕西省科研计划项目(No.21JK0554)第一作者:鲁博(1982—),男,汉族,陕西省咸阳人,讲师,主要研究领域为智能控制。E-mail:lubo@stu.kust.edu.cn通信作者:张寿明(1965—),男,白族,云南昆明人,教授,硕士研究生导师,主要研究领域为复杂工业控制。
更新日期/Last Update: 1900-01-01