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

被引：0
|
作者：
Chai T.-Y. [1 ,2 ]
Zhou Z. [1 ]
Zheng R. [1 ]
Liu N. [1 ]
Jia Y. [1 ,2 ]
机构：
[1] State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang
[2] National Engineering Research Center of Metallurgy Automation, Shenyang
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2023年 / 49卷 / 03期
基金：
中国国家自然科学基金;
关键词：
deep learning; end-edge-cloud collaboration technology; intelligent system; PID parameter tuning; reinforcement learning;
D O I：
10.16383/j.aas.c230055
中图分类号：
学科分类号：
摘要：
Based on the analysis of the new requirements of intelligent manufacturing for PID tuning and the challenges and difficulties faced by PID tuning, this paper proposes an adaptive and autonomous PID tuning intelligent optimization method by deeply integrates and coordinates the modeling, control and optimization in automation and deep learning and reinforcement learning in artificial intelligence. The proposed method contains the digital twin model of the PID control process based on end-edge-cloud collaboration and the PID tuning algorithm combining reinforcement learning and digital twin model. Furthermore, the PID tuning intelligent system is developed by combining the end-edge-cloud collaboration technology of Industrial Internet with the PLC control system, and has been successfully applied to the energy intensive equipment — Fused magnesium furnace. This system operates safely, reliably and optimally, achieving remarkable effects in energy conservation and emission reduction. Finally, the further research content in the intelligent research direction of control system is proposed. © 2023 Science Press. All rights reserved.
引用
收藏
页码：514 / 527
页数：13
相关论文
共 59 条
  • [1] Guo L., Feedback and uncertainty: Some basic problems and results, Annual Reviews in Control, 49, pp. 27-36, (2020)
  • [2] Samad T., A survey on industry impact and challenges thereof [technical activities], IEEE Control Systems Magazine, 37, 1, (2017)
  • [3] Borase R, Maghade D K, Sondkar S Y, Pawar S N., A review of PID control, tuning methods and applications, International Journal of Dynamics and Control, 9, 2, pp. 818-827, (2021)
  • [4] Astrom K J, Hagglund T., PID Controllers: Theory, Design, and Tuning (Second edition), (1995)
  • [5] Somefun O A, Akingbade K, Dahunsi F., The dilemma of PID tuning, Annual Reviews in Control, 52, pp. 65-74, (2021)
  • [6] Ziegler J G, Nichols N B., Optimum settings for automatic controllers, Transactions of the ASME, 64, 8, pp. 759-765, (1942)
  • [7] Astrom K J, Hagglund T., Automatic tuning of simple regulators with specifications on phase and amplitude margins, Automatica, 20, 5, pp. 645-651, (1984)
  • [8] Ho W K, Lim K W, Xu W., Optimal gain and phase margin tuning for PID controllers, Automatica, 34, 8, pp. 1009-1014, (1998)
  • [9] Ho W K, Lim K W, Hang C C, Ni L Y., Getting more phase margin and performance out of PID controllers, Automatica, 35, 9, pp. 1579-1585, (1999)
  • [10] Silva G J, Datta A, Bhattacharyya S P., On the stability and controller robustness of some popular PID tuning rules, IEEE Transactions on Automatic Control, 48, 9, pp. 1638-1641, (2003)
123456