PID controller combined with intelligent algorithm

Research Article
Open access

PID controller combined with intelligent algorithm

Yuxuan Sun 1*
  • 1 School of Mechanical and Electronic Control Engineering, Beijing Jiaotong University, Beijing, China, 100089    
  • *corresponding author sunyuxuan2012@163.com
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230868
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

PID controller has a wide range of applications in industrial control due to its suitability for a variety of system types, easy operation, and effective results. With the advancement of technology, however, industrial production is placing a greater emphasis on automation, intelligence, and PID controllers, as it is difficult to adapt linear time-invariant systems to the control requirements of current industrial production. This research examines the optimization and transformation of PID controller using a range of clever algorithms by conducting a literature review. PID controller paired with an intelligence algorithm has superior transient and steady-state performance compared to standard PID when dealing with nonlinear systems that are more complex and difficult to predict. This paper's research reveals the general optimization concept and direction of self-tuning PID with an intelligent algorithm.

Keywords:

PID Controller, Fuzzy Control Theory, Artificial Neural Network, Group Intelligence Algorithm.

Sun,Y. (2023). PID controller combined with intelligent algorithm. Applied and Computational Engineering,6,490-496.
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References

[1]. WANG Ming. Design and Simulation of a PID Parameter Self-Tuning Controller Based on Fuzzy Control Theory [J]. Automation and instrumentation, 2000 (01) : 16-19. DOI: 10.14016 / j.carol carroll nki. 1001-9227.2000.01.007.

[2]. Zheng meiru.Optimal design of PID control algorithm for manipulator based on fuzzy control theory[J].Industrial Heating,2021,50(9):41-44.

[3]. Guan Sheng-qi et al.Self-tuning fuzzy PID control for grasping force of rope driven manipulator[J].Journal of Xi’an Polytechnic University,2021,35(6):96-103.

[4]. XieDengYu. The design of PID controller based on neural network [J]. Science Tribune (below the ten-day), 2020 (21) : 74-75. The DOI: 10.16400 / j.carol carroll nki KJDKX. 2020.07.034.

[5]. R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proc. 6th Int. Symp. Micro Mach. Human Sci., 1995, pp. 39–43.

[6]. H. Mustafifidah and Suwarsito, ‘‘Correlation analysis between error rate of output and learning rate in backpropagation network,’’ Adv. Sci. Lett., vol. 24, no. 12, pp. 9182–9185, Dec. 2018, doi: 10.1166/asl.2018.12121.

[7]. O. Rodríguez-Abreo, J. Rodríguez-Reséndiz, C. Fuentes-Silva, R. Hernández-Alvarado and M. D. C. P. T. Falcón, "Self-Tuning Neural Network PID With Dynamic Response Control," in IEEE Access, vol. 9, pp. 65206-65215, 2021, doi: 10.1109/ACCESS.2021.3075452.

[8]. Li Feng, Fan Yuhe, Liang Hui. Research on Temperature and Humidity Control of Greenhouse Based on improved BP Neural Network PID Controller [J]. Computer and Digital Engineering,2021,49(05):908-913+986.

[9]. Gani, M.M., Islam, M.S. & Ullah, M.A. Optimal PID tuning for controlling the temperature of electric furnace by genetic algorithm.SN Appl. Sci.1, 880 (2019). https://doi.org/10.1007/s42452-019-0929-y

[10]. J. G. Ziegler and N. B. Nichols, “Optimal settings for automatic controllers,” Trans. ASME, vol. 64, pp. 759–768, 1942.

[11]. Sinlapakun V, Assawinchaichote W (2015) Optimized PID controller design for electric furnace temperature systems with Nelder Mead Algorithm. In: 12th international conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON), 24–27 June 2015, pp 1–4.

Cite this article

Sun,Y. (2023). PID controller combined with intelligent algorithm. Applied and Computational Engineering,6,490-496.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. WANG Ming. Design and Simulation of a PID Parameter Self-Tuning Controller Based on Fuzzy Control Theory [J]. Automation and instrumentation, 2000 (01) : 16-19. DOI: 10.14016 / j.carol carroll nki. 1001-9227.2000.01.007.

[2]. Zheng meiru.Optimal design of PID control algorithm for manipulator based on fuzzy control theory[J].Industrial Heating,2021,50(9):41-44.

[3]. Guan Sheng-qi et al.Self-tuning fuzzy PID control for grasping force of rope driven manipulator[J].Journal of Xi’an Polytechnic University,2021,35(6):96-103.

[4]. XieDengYu. The design of PID controller based on neural network [J]. Science Tribune (below the ten-day), 2020 (21) : 74-75. The DOI: 10.16400 / j.carol carroll nki KJDKX. 2020.07.034.

[5]. R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proc. 6th Int. Symp. Micro Mach. Human Sci., 1995, pp. 39–43.

[6]. H. Mustafifidah and Suwarsito, ‘‘Correlation analysis between error rate of output and learning rate in backpropagation network,’’ Adv. Sci. Lett., vol. 24, no. 12, pp. 9182–9185, Dec. 2018, doi: 10.1166/asl.2018.12121.

[7]. O. Rodríguez-Abreo, J. Rodríguez-Reséndiz, C. Fuentes-Silva, R. Hernández-Alvarado and M. D. C. P. T. Falcón, "Self-Tuning Neural Network PID With Dynamic Response Control," in IEEE Access, vol. 9, pp. 65206-65215, 2021, doi: 10.1109/ACCESS.2021.3075452.

[8]. Li Feng, Fan Yuhe, Liang Hui. Research on Temperature and Humidity Control of Greenhouse Based on improved BP Neural Network PID Controller [J]. Computer and Digital Engineering,2021,49(05):908-913+986.

[9]. Gani, M.M., Islam, M.S. & Ullah, M.A. Optimal PID tuning for controlling the temperature of electric furnace by genetic algorithm.SN Appl. Sci.1, 880 (2019). https://doi.org/10.1007/s42452-019-0929-y

[10]. J. G. Ziegler and N. B. Nichols, “Optimal settings for automatic controllers,” Trans. ASME, vol. 64, pp. 759–768, 1942.

[11]. Sinlapakun V, Assawinchaichote W (2015) Optimized PID controller design for electric furnace temperature systems with Nelder Mead Algorithm. In: 12th international conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON), 24–27 June 2015, pp 1–4.

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