|本期目录/Table of Contents|

[1]张 皓,高瑜翔**,唐 军,等.基于T-S型模糊神经网络的内模自适应PID算法研究[J].工业仪表与自动化装置,2021,(06):118-124.[doi:10.19950/j.cnki.cn61-1121/th.2021.06.024]
　ZHANG Hao,GAO Yuxiang*,TANG Jun,et al.Research on internal model adaptive PID algorithm based on T-S Fuzzy neural network[J].Industrial Instrumentation & Automation,2021,(06):118-124.[doi:10.19950/j.cnki.cn61-1121/th.2021.06.024]
点击复制

基于T-S型模糊神经网络的内模自适应PID算法研究

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

卷:
期数:
2021年06期
页码:
118-124
栏目:
出版日期:
2021-12-15

文章信息/Info

Title:
Research on internal model adaptive PID algorithm based on T-S Fuzzy neural network
作者:
张 皓1; 2; 高瑜翔**1; 2; 唐 军3; 杜鑫昌1; 2; 刘海波1; 2
（1.成都信息工程大学通信工程学院；2.气象信息与信号处理四川省高校重点实验室，四川 成都 610225；3.宜宾职业技术学院电子信息与人工智能学院，四川 宜宾 644000）
Author(s):
ZHANG Hao1; 2;  GAO Yuxiang*1; 2 ;  TANG Jun3;  DU Xinchang1; 2;  LIU Haibo1; 2
(1.College of Communication Engineering, Chengdu University of Information Technology；2. Meteorological Information and Signal Processing Key Laboratory of Sichuan Education Institutes, Sichuan Chengdu 610225, China；3.College of Electronic Information and
关键词:
内模PID; 大滞后; 温度模型; T-S型模糊神经网络
Keywords:
Internal model PID;  Large lag;  Temperature model;  T-S type fuzzy neural network
分类号:
TP273+.3；TP273+.4
DOI:
10.19950/j.cnki.cn61-1121/th.2021.06.024
文献标志码:
A
摘要:
针对内模PID算法在控制具有大滞后特性的温度模型时，因内模参数无法根据内部模型的失配程度进行自适应调整，使得系统超调量增大，调节时间变长，控制响应不及时，控制品质下降等问题。该文提出了基于T-S型模糊神经网络的内模自适应PID算法，大大降低上述问题对系统控制性能指标的影响。Matlab仿真结果表明：在内部模型与被控模型的参数出现正向失配或负向失配时，该文算法控制响应最快、超调量最小，控制精度最高，具有最好的控制性能指标。
Abstract:
When the internal model PID algorithm is controlling a temperature model with large hysteresis characteristics, because the internal model parameters cannot be adjusted adaptively according to the degree of mismatch of the internal model, the system overshoot increases, the adjustment time becomes longer, and the control response is not timely, Control the quality degradation and other issues. This paper proposes an internal model adaptive PID algorithm based on the T-S fuzzy neural network, which greatly reduces the impact of the above problems on the system control performance indicators. The Matlab simulation results show that when the parameters of the internal model and the controlled model are positively mismatched or negatively mismatched, the algorithm in this paper has the fastest control response, the smallest overshoot, the highest control accuracy, and the best control performance index.

参考文献/References:

[1]张皓,高瑜翔.前馈反馈Smith预估模糊PID组合温度控制算法[J].中国测试,2020,46(11):132-138+168.

[2]Li H S, Li R X, Wang F. A new control performance evaluation based on LQG benchmark for the heating furnace temperature control system[J]. Processes,2020,8(11).
[3]PU C Z, SUN X J. Research on temperature control of box-type resistance furnace based on fuzzy PID[J]. International core journal of engineering,2021,7(1).
[4]Jin A G, Wu H X, Zhu H S, Hua H, Hu Y H. Design of temperature control system for infant radiant warmer based on Kalman filter-fuzzy PID[J]. Journal of physics: conference series,2020,1684(1).
[5]赵志诚,文新宇.内模控制及其应用[M].北京:电子工业出版社,2012:2.
[6]Leitner S, Yazdanian M, Ziaeinejad S, et al. Internal Model-Based Active Resonance Damping Current Control of a Grid-Connected Voltage-Sourced Converter With an LCL Filter[J]. IEEE Transactions on Power Systems, 2018, 33(6):6025-6036.
[7]孙曙光,林雪松,杜太行,等.高频开关电源模糊内模PID控制器设计[J].电测与仪表,2015,52(21):106-111.
[8]罗启平,李婷.基于模糊内模的肥料包装机称量控制系统设计[J].包装工程,2020,41(21):219-223.
[9]NATH U M, DEY C C, MUDI R K. Fuzzy rule-based auto-tuned internal model controller for real-time experimentation on temperature and level processes[J]. International journal of automation and control,2020,14(2).
[10]陈彦涛.基于模糊设定值加权IMC-PID算法的纸张定量控制[J].包装工程,2018,39(21):157-162.
[11]蒋念平,张琪.基于神经网络的IMC-PID超声波电机控制[J].电子科技,2015,28(12):139-142.
[12]陶征勇,童仲志,侯远龙,等.基于RBF神经网络的破障武器内模PID控制[J].电气自动化,2020,42(05):87-89+95.
[13]张新良,朱琳,郭晓迪.一种频率依赖性迟滞的神经网络内模控制[J].测控技术,2016,35(09):52-54+58.
[14]句建国,邢进生,王冬冬.基于BP-NN的热连轧产品性能自适应逆控制模型[J].计算机技术与发展,2018,28(12):185-189.
[15]ZHANG Y J, FANG X. The hydraulic system of intelligent Asphalt Disperser Based on Neural Network Internal Model control[J]. Applied mechanics and materials,2014,3072.
[16]ABDUL W N, MAHMOD N, VILANOVA R. Permeate Flux Control in SMBR System by Using Neural Network Internal Model Control[J]. Processes,2020,8(12).
[17]马海涛,宁楠,陈兆波.基于改进型模糊神经网络的污水处理溶解氧内模控制[J].计算机应用与软件,2020,37(10):41-46+90.
[18]王文庆,杨振新,唐轩.模糊系统作为通用逼近器研究综述[J].西安邮电大学学报,2015,20(01):1-8.
[19]王再英,刘淮霞,陈毅静.过程控制系统与仪表[M].北京:机械工业出版社,2006:228.

相似文献/References:

备注/Memo

备注/Memo:
收稿日期：2021-07-20
基金项目：四川省教育厅高校创新团队项目(15TD0022)
作者简介：张皓(1992)，男，硕士研究生，主要研究领域为人工智能与智能信息处理。
通信作者：高瑜翔(1970)，男，博士，教授，主要研究领域为无线通信技术与系统、人工智能与智能信息处理。
更新日期/Last Update: 1900-01-01