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端边云协同的PID整定智能系统

柴天佑 周正 郑锐 刘宁 贾瑶

柴天佑, 周正, 郑锐, 刘宁, 贾瑶. 端边云协同的PID整定智能系统. 自动化学报, 2023, 49(3): 514−527 doi: 10.16383/j.aas.c230055
引用本文: 柴天佑, 周正, 郑锐, 刘宁, 贾瑶. 端边云协同的PID整定智能系统. 自动化学报, 2023, 49(3): 514−527 doi: 10.16383/j.aas.c230055
Chai Tian-You, Zhou Zheng, Zheng Rui, Liu Ning, Jia Yao. PID tuning intelligent system based on end-edge-cloud collaboration. Acta Automatica Sinica, 2023, 49(3): 514−527 doi: 10.16383/j.aas.c230055
Citation: Chai Tian-You, Zhou Zheng, Zheng Rui, Liu Ning, Jia Yao. PID tuning intelligent system based on end-edge-cloud collaboration. Acta Automatica Sinica, 2023, 49(3): 514−527 doi: 10.16383/j.aas.c230055

端边云协同的PID整定智能系统

doi: 10.16383/j.aas.c230055
基金项目: 国家自然科学基金委重大项目(61991404), 2020年度辽宁省科技重大专项计划(2020JH1/10100008), 一体化过程控制学科创新引智基地2.0 (B08015)
详细信息
    作者简介:

    柴天佑:中国工程院院士, 东北大学教授. IEEE Life Fellow, IFAC Fellow, 欧亚科学院院士. 主要研究方向为自适应控制, 智能解耦控制, 流程工业综合自动化与智能化系统理论、方法与技术. 本文通信作者. E-mail: tychai@mail.neu.edu.cn

    周正:东北大学流程工业综合自动化国家重点实验室博士研究生. 主要研究方向为决策与控制一体化智能系统技术研究. E-mail: 17862716659@163.com

    郑锐:东北大学流程工业综合自动化国家重点实验室博士研究生. 主要研究方向为智能控制技术, 决策与控制一体化智能系统技术. E-mail: 2010263@stu.neu.edu.cn

    刘宁:东北大学流程工业综合自动化国家重点实验室博士研究生. 2019年获得中北大学硕士学位. 主要研究方向为控制理论与技术. E-mail: 18735135253@163.com

    贾瑶:东北大学流程工业综合自动化国家重点实验室讲师. 主要研究方向为智能运行控制技术, 智能控制技术, 智能检测技术, 决策与控制一体化智能系统技术. E-mail: jiayao@mail.neu.edu.cn

PID Tuning Intelligent System Based on End-edge-cloud Collaboration

Funds: Supported by National Natural Science Foundation of China (61991404), Science and Technology Major Project 2020 of Liaoning Province (2020JH1/10100008), and 111 Project 2.0 (B08015)
More Information
    Author Bio:

    CHAI Tian-You Academician of Chinese Academy of Engineering, professor at Northeastern University, IEEE Life Fellow, IFAC Fellow, and academician of the International Eurasian Academy of Sciences. His research interest covers adaptive control, intelligent decoupling control, and theories, methods and technology of synthetical automation and intelligent system for process industries. Corresponding author of this paper

    ZHOU Zheng Ph.D. candidate at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. His research interest covers intelligent system technology integrating decision-making and control

    ZHENG Rui Ph.D. candidate at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. His research interest covers intelligent control technology, and intelligent system technology integrating decision-making and control

    LIU Ning Ph.D. candidate at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. He received his master degree from North University of China in 2019. His research interest covers control theory and technology

    JIA Yao  Lecturer at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. His research interest covers intelligent operation control technology, intelligent control technology, intelligent detection technology, and intelligent system technology integrating decision-making and control

  • 摘要: 本文在分析智能制造对PID整定的新需求及PID整定面临的挑战难题的基础上, 将自动化的建模、控制与优化和人工智能的深度学习与强化学习深度融合与协同, 提出了自适应与自主的PID整定的智能优化方法, 包括端边云协同的PID控制过程数字孪生模型和强化学习与数字孪生模型相结合的PID整定算法. 将工业互联网的端边云协同技术与PLC控制系统相结合, 研制了PID整定智能系统, 并在重大耗能设备 — 电熔镁炉成功应用. 该系统安全、可靠与优化运行, 取得显著的节能减排效果. 最后, 提出了控制系统智能化研究方向需要进一步深入研究的内容.
  • 图  1  PID控制过程与数字孪生模型

    Fig.  1  PID control process and digital twin model

    图  2  端边云协同的PID控制过程数字孪生模型结构

    Fig.  2  The structure of the digital twin model of PID control process based on end-edge-cloud collaboration

    图  3  PID控制器参数整定结构

    Fig.  3  The structure of PID controller parameters tuning

    图  4  基于强化学习的PID整定结构

    Fig.  4  The structure of PID tuning based on reinforcement learning

    图  5  PID整定智能系统功能与架构

    Fig.  5  The function and architecture of PID tuning intelligent system

    图  6  电熔镁炉PID控制过程

    Fig.  6  PID control process of fused magnesia furnace

    图  7  ${\bar{v}_i}(k)$的自适应深度学习模型的长短周期记忆网络架构

    Fig.  7  The long short-term memory architecture of the adaptive deep learning model of ${\bar{v}_i}(k) $

    图  8  电流实际值和数字孪生模型输出曲线

    Fig.  8  The curves of the actual current value and the output of the digital twin model

    图  9  三相电流PID控制器性能指标和整定参数迭代曲线

    Fig.  9  The iterative curves of the performance index and the tunning parameters of the current PID controllers for three phases

    图  10  端边云协同的电熔镁炉PID整定智能系统结构

    Fig.  10  The structure for PID tuning intelligent system of fused magnesia furnace based on end-edge-cloud collaboration

    图  11  采用常规PID控制算法时电极电流$ {y_i}(k)$、熔化电流设定值$ {y_{sp}}(k)$和控制输入$ {u_i}(k)$的曲线

    Fig.  11  The curves of electrode current ${y_i}(k) $, melting current setting ${y_{sp}}(k) $ and control input ${u_i}(k) $ with the conventional PID control algorithm

    图  12  采用本文所提优化整定PID控制算法时电极电流$ {y_i}(k)$、熔化电流设定值$ {y_{sp}}(k)$和控制输入$ {u_i}(k)$的曲线

    Fig.  12  The curves of electrode current ${y_i}(k) $, melting current setting $ {y_{sp}}(k) $ and control input $ {u_i}(k) $ with the proposed algorithm

    表  1  数字孪生模型精度评价表

    Table  1  The evaluation table of the accuracy of the digital twin model

    云数字孪生模型边数字孪生模型
    MAERMSEMAERMSE
    y1(k)429.144535.359466.751651.642
    y2(k)369.998474.175375.679487.996
    y3(k)341.209450.719363.354451.023
    下载: 导出CSV

    表  2  常规PID方法和本文方法的控制性能评价表(N = 1000)

    Table  2  The evaluation table of the control performance of the conventional PID method and the proposed method

    MSE×10−6IAE×10−6
    ${e_1}(k)$${e_2}(k)$${e_3}(k)$${e_1}(k)$${e_2}(k)$${e_3}(k)$
    常规PID方法1.25791.50441.43322.77053.76293.3114
    本文方法0.67320.71070.84621.25791.50441.4332
    下载: 导出CSV
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  • 收稿日期:  2023-02-15
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