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[1]雷翔霄?,徐立娟,唐春霞.基于神经网络PID算法的镀液温度控制系统[J].电镀与精饰,2020,(8):39-42.[doi:10.3969/j.issn.1001-3849.2020.08.0080]
　LEI Xiangxiao,XU Lijuan,et al.Bath Temperature Control System Based on Neural Network PID[J].Plating & Finishing,2020,(8):39-42.[doi:10.3969/j.issn.1001-3849.2020.08.0080]

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基于神经网络PID算法的镀液温度控制系统

《电镀与精饰》[ISSN:1001-3849/CN:12-1096/TG] 卷: 期数: 2020年8 页码: 39-42 栏目: 出版日期: 2020-08-10

Title:: Bath Temperature Control System Based on Neural Network PID

作者:: 雷翔霄¹; 2?; 徐立娟¹; 唐春霞¹; 1.长沙民政职业技术学院，湖南长沙 410004； 2.湖南大学电气与信息工程学院，湖南长沙 410082

Author(s):: LEI Xiangxiao¹; 2; ; XU Lijuan¹; TANG Chunxia¹; 1. Changsha Social Work College, Changsha 410004, China　2. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

关键词:: 电镀; BP神经网络; PID算法; BP-PID算法

Keywords:: electroplating BP neural network PID algorithm BP-PID algorithm

DOI:: 10.3969/j.issn.1001-3849.2020.08.0080

文献标志码:: A

摘要:: 设计了电镀槽液的温度控制系统，采用BP神经网络改进PID算法，实现了获得近似最优控制参数，并将BP-PID算法应用于电镀槽液温控系统中。在MATLAB中仿真实验表明，BP-PID控制算法能快速的整定出最优PID参数，并使系统达到较好的控制效果。实际应用表明：当控制温度为60 ℃时，BP-PID算法与常规PID算法相比具有更好的控温效果，超调率小于3%，稳态精度小于±1.5 ℃。

Abstract:: A temperature control system of electroplating solution was designed. BP neural network was used to improve the PID algorithm to achieve the approximate optimal control parameters, and the BP-PID algorithm was applied to the solution temperature control system. The simulation experiment in MATLAB showed that the BP-PID algorithm could adjust the optimal PID parameters quickly, and make the system achieve a preferable control effect. The practical application showed that the BP-PID algorithm had better temperature control effect than the conventional PID algorithm when the temperature was 60 ℃. The overshoot rate was less than 4%, and the steady-state accuracy was less than 1.5 ℃.

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备注/Memo

收稿日期： 2020-01-18；修回日期： 2020-05-06
作者简介：雷翔霄，博士，副教授，研究方向：智能控制、优化算法等；Email：305444938@qq.com
基金项目：湖南省自然科学基金（2019JJ70005），湖南省职业院校教育教学改革研究项目(ZJGB2019002)，国家自然科学基金（51677063）。

更新日期/Last Update: 2020-08-10