Devising a combined method for setting PI/PID controller parameters for oil and gas facilities

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.322424

Keywords:

control system, combined criterion, PI/PID controller, tuning parameters, local minimum

Abstract

The object of this study is automatic control systems of the first, second, and third orders. The principal task was to ensure the stability of control systems while minimizing overshot and regulation time.

A combined method for determining the tuning parameters of PI/PID controllers has been devised, which combines the s-plane method and the generalized quadratic criterion.

The s-plane method is based on the Vieta theorem, which relates the roots of the characteristic equation of a closed-loop control system to its parameters. They are functions of the tuning parameters of PI/PID controllers. By choosing the left roots of the characteristic equation of a closed-loop system on the s-plane, the desired quality indicators of the control system can be achieved. The roots of the equation are functionally related to the parameters of PI/PID controllers. From the system of algebraic equations that follow from the Vieta theorem, the tuning parameters for PI/PID controllers are found as a solution to such a system.

At the second stage of solving the problem, the roots of the characteristic equation are chosen so that the generalized quadratic criterion is a function only of real part of one of the characteristic equation’s roots. As a result, we obtain a one-dimensional minimization problem, the local minimum of which was sought within a predetermined search interval. This interval was chosen on the condition that the parameters for PI/PID controllers would be strictly positive. The roots of the characteristic equation of the closed-loop system would belong to the left half-plane of the s-plane. Such a choice of the search interval guarantees the stability of the closed-loop automatic control system.

It was found that compared to the s-plane method, the overshot and regulation time were reduced by an average of 73.5 % and 66.5 %. This could increase the speed of industrial controllers

Author Biographies

Mykhailo Horbiichuk, Ivano-Frankivsk National Technical University of Oil and Gas

Doctor of Technical Sciences

Department of Automation and Computer-Integrated Technologies

Mykhailo Vasylenchuk, Ivano-Frankivsk National Technical University of Oil and Gas

PhD Student

Department of Automation and Computer-Integrated Technologies

Ihor Yednak, Ivano-Frankivsk National Technical University of Oil and Gas

PhD Student

Department of Automation and Computer-Integrated Technologies

Andrii Lahoida, Ivano-Frankivsk National Technical University of Oil and Gas

PhD

Department of Automation and Computer-Integrated Technologies

References

  1. Borase, R. P., Maghade, D. K., Sondkar, S. Y., Pawar, S. N. (2020). A review of PID control, tuning methods and applications. International Journal of Dynamics and Control, 9 (2), 818–827. https://doi.org/10.1007/s40435-020-00665-4
  2. Coelho, L. dos S., Mariani, V. C. (2012). Firefly algorithm approach based on chaotic Tinkerbell map applied to multivariable PID controller tuning. Computers & Mathematics with Applications, 64 (8), 2371–2382. https://doi.org/10.1016/j.camwa.2012.05.007
  3. Kanojiya, R. G., Meshram, P. M. (2012). Optimal tuning of PI controller for speed control of DC motor drive using particle swarm optimization. 2012 International Conference on Advances in Power Conversion and Energy Technologies (APCET), 1–6. https://doi.org/10.1109/apcet.2012.6302000
  4. Ali, A., Majhi, S. (2010). PID controller tuning for integrating processes. ISA Transactions, 49 (1), 70–78. https://doi.org/10.1016/j.isatra.2009.09.001
  5. Thomsen, S., Hoffmann, N., Fuchs, F. W. (2011). PI Control, PI-Based State Space Control, and Model-Based Predictive Control for Drive Systems With Elastically Coupled Loads – A Comparative Study. IEEE Transactions on Industrial Electronics, 58 (8), 3647–3657. https://doi.org/10.1109/tie.2010.2089950
  6. Precup, R.-E., Angelov, P., Costa, B. S. J., Sayed-Mouchaweh, M. (2015). An overview on fault diagnosis and nature-inspired optimal control of industrial process applications. Computers in Industry, 74, 75–94. https://doi.org/10.1016/j.compind.2015.03.001
  7. Joseph, S. B., Dada, E. G., Abidemi, A., Oyewola, D. O., Khammas, B. M. (2022). Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems. Heliyon, 8 (5), e09399. https://doi.org/10.1016/j.heliyon.2022.e09399
  8. Stavrov, D., Nadzinski, G., Deskovski, S., Stankovski, M. (2021). Quadratic Model-Based Dynamically Updated PID Control of CSTR System with Varying Parameters. Algorithms, 14 (2), 31. https://doi.org/10.3390/a14020031
  9. Na, S., Anitescu, M., Kolar, M. (2023). Inequality constrained stochastic nonlinear optimization via active-set sequential quadratic programming. Mathematical Programming, 202 (1-2), 279–353. https://doi.org/10.1007/s10107-023-01935-7
  10. Jayachitra, A., Vinodha, R. (2014). Genetic Algorithm Based PID Controller Tuning Approach for Continuous Stirred Tank Reactor. Advances in Artificial Intelligence, 2014, 1–8. https://doi.org/10.1155/2014/791230
  11. Saad, M. S., Jamaluddin, H., Darus, I. Z. M. (2012). Implementation of PID Controller tuning using Differential Evolution and Genetic Algorithms. Computing, Information and Control, 8 (11). Available at: https://www.researchgate.net/profile/Jyotindra-Narayan/post/How-to-tune-PID-gains-in-control-algorithm-using-particle-swarm-optimization-or-genetic-algorithm-to-minimize-the-robot-trajectory-tracking-errors/attachment/5ef2cf493f8af70001ebe5b1/AS%3A905800520318976%401592971081601/download/Implementation_of_PID_controller_tuning.pdf
  12. Cao, F. (2018). PID controller optimized by genetic algorithm for direct-drive servo system. Neural Computing and Applications, 32 (1), 23–30. https://doi.org/10.1007/s00521-018-3739-z
  13. Solihin, M. I., Tack, L. F., Kean, M. L. (2011). Tuning of PID Controller Using Particle Swarm Optimization (PSO). Proceeding of the International Conference on Advanced Science, Engineering and Information Technology. Available at: https://www.researchgate.net/profile/Lip-Kean-Moey/publication/251442573_Tuning_of_PID_Controller_Using_Particle_Swarm_Optimization_PSO/links/5b1f1d60458515270fc475db/Tuning-of-PID-Controller-Using-Particle-Swarm-Optimization-PSO.pdf
  14. Wang, D., Tan, D., Liu, L. (2017). Particle swarm optimization algorithm: an overview. Soft Computing, 22 (2), 387–408. https://doi.org/10.1007/s00500-016-2474-6
  15. Ribeiro, J. M. S., Santos, M. F., Carmo, M. J., Silva, M. F. (2017). Comparison of PID controller tuning methods: analytical/classical techniques versus optimization algorithms. 2017 18th International Carpathian Control Conference (ICCC), 533–538. https://doi.org/10.1109/carpathiancc.2017.7970458
  16. Li, S., Wei, Y., Liu, X., Zhu, H., Yu, Z. (2022). A New Fast Ant Colony Optimization Algorithm: The Saltatory Evolution Ant Colony Optimization Algorithm. Mathematics, 10 (6), 925. https://doi.org/10.3390/math10060925
  17. Niu, B., Wang, H. (2012). Bacterial Colony Optimization. Discrete Dynamics in Nature and Society, 2012 (1). https://doi.org/10.1155/2012/698057
  18. Sagban, R., Marhoon, H. A., Alubady, R. (2020). Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care. International Journal of Electrical and Computer Engineering (IJECE), 10 (6), 6655–6663. https://doi.org/10.11591/ijece.v10i6.pp6655-6663
  19. Shami, T. M., El-Saleh, A. A., Alswaitti, M., Al-Tashi, Q., Summakieh, M. A., Mirjalili, S. (2022). Particle Swarm Optimization: A Comprehensive Survey. IEEE Access, 10, 10031–10061. https://doi.org/10.1109/access.2022.3142859
  20. Bardavelidze, A., Bardavelidze, K. (2024). Development and investigation of algorithm for the synthesis of an automatic control system of the drying process. International Journal on Information Technologies and Security, 16 (1), 15–26. https://doi.org/10.59035/ojbv6115
  21. Chopra, V., Singla, S. K., Dewan, L. (2014). Comparative Analysis of Tuning a PID Controller using Intelligent Methods. Acta Polytechnica Hungarica, 11 (8), 235–249. https://doi.org/10.12700/aph.11.08.2014.08.13
  22. Horbiychuk, M., Lazoriv, N., Chyhur, L., Chyhur, І. (2021). Determining configuration parameters for proportionally integrated differentiating controllers by arranging the poles of the transfer function on the complex plane. Eastern-European Journal of Enterprise Technologies, 5 (2 (113)), 80–93. https://doi.org/10.15587/1729-4061.2021.242869
  23. Ventre, A. G. S. (2023). Determinants and Systems of Linear Equations. Calculus and Linear Algebra. Cham: Springer, 209–241. https://doi.org/10.1007/978-3-031-20549-1_14
  24. Borovska, T. M. (2018). Teorіia avtomatichnogo upravlіnnia. Vіnnitcia: Vіnnitckii natcіonalnii tekhnіchnii unіversitet, 256. Available at: https://pdf.lib.vntu.edu.ua/books/IRVC/2021/Borovska_2018_256.pdf
  25. Newton, Jr., Gould, L. A., Kaiser, J. F. (1957). Analytic design of linear feedback controls. New York, 419.
Devising a combined method for setting PI/PID controller parameters for oil and gas facilities

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Published

2025-02-27

How to Cite

Horbiichuk, M., Vasylenchuk, M., Yednak, I., & Lahoida, A. (2025). Devising a combined method for setting PI/PID controller parameters for oil and gas facilities. Eastern-European Journal of Enterprise Technologies, 1(2 (133), 85–95. https://doi.org/10.15587/1729-4061.2025.322424