OPTIMIZATION OF PID CONTROLLER PARAMETERS USING MACHINE LEARNING ALGORITHMS BASED ON OIL SEPARATION PROCESS DATA

This paper presents the investigation of the process of optimizing the parameters of a PID controller using machine learning algorithms for the oil separation process control system. The optimization of the controller parameters (Kp, Ki, Kd) is important, in order to improve control quality and reduce the number of errors in dynamic processes. To solve this issue, several innovative methods were considered, such as the cuckoo search algorithm (CSA), the firefly algorithm (FA), particle swarm optimization (PSO), and the support vector machine (SVM). All the data, including the current process values (PV), setpoints (SP) and output signals (OP) were obtained from Tengizchevroil. In addition, the metrics, such as root-mean-square error (MSE), adjustment time, overshoot, and steady-state error were used to assess the effectiveness of optimized regulators. Overall, the results of the research indicate that there was a significant improvement of the dynamic characteristics of the system due to the usage of machine learning algorithms compared to the traditional approaches. The obtained parameters of optimization achieved the target value while being faster and more stable, thus increasing the productivity of control in the technological process.

1. Sathasivam L., Elamvazuthi I., Ahamed Khan M. K. A., & Parasuraman S. Tuning A Three-Phase Separator Level Controller via Particle Swarm Optimization Algorithm // Proceedings of the 2018 International Conference on Recent Trends in Electrical, Control and Communication (RTECC). – IEEE, 2018. – P. 265–268.

2. Reis V.C., Santos M.F., Carmo M.J., Ferreira F.C. Control of Level in Systems Based on Arduino Platform through PC // Proceedings of the 20th International Conference on System Theory, Control and Computing (ICSTCC). – Sinaia, Romania: IEEE, 2016. – P. 251–256.

3. Charkoutsis S., Kara-Mohamed M. Particle Swarm Optimization tuned nonlinear PID controller with improved performance and robustness for First Order Plus Time Delay systems // Results in Control and Optimization. – 2023. – Vol. 12. – P. 100289.

4. Foss B. Process control in conventional oil and gas production: Challenges and opportunities // Control Engineering Practice. – 2012. – Vol. 20. – No. 10. – P. 1058–1064.

5. Patel V. V. Ziegler-nichols tuning method: Understanding the pid controller // Resonance. – 2020. – Vol. 25. – No. 10. – P. 1385–1397.

6. Ribeiro J.M.S., Santos M.F., Carmo M.J., Silva M.F. Comparison of PID Controller Tuning Methods: Analytical/Classical Techniques versus Optimization Algorithms // Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (ICSMC). – 2017. – P. 533–538.

7. Garpinger O., Hägglund T., Åström K. J. Performance and robustness trade-offs in PID control // Journal of Process Control. – 2014. – Vol. 24. – No. 5. – P. 748–760.

8. Shahrokhi M., Zamorrodi A. Comparison of PID controller adjustment methods // Journal of Process Control. – 2024. – No. 2. – P. 1–15.

9. Eltayeb A., Ahmed G., Imran I.H., Alyazidi N.M., Abubaker A. Comparative Analysis: Fractional PID vs. PID Controllers for Robotic Arm Using Genetic Algorithm Optimization // Automation. – 2024. – Vol. 5. – P. 230–245.

10. Hekimoğlu B. Optimal tuning of fractional order PID controller for DC motor speed control via chaotic atom search optimization algorithm // IEEE Access. – 2019. – Vol. 7. – P. 38100–38114.

11. Mehta S.A., Katrodiya J., Mankad B. Simulation, Design and Practical Implementation of IMC Tuned Digital PID Controller for Liquid Level Control System // Proceedings of the International Conference on Current Trends in Technology (NUiCONE-2011). Ahmedabad, India: IEEE, 2011. – P. 1–5.

12. Alsmadi M.K., Mohammad R.M.A., Alzaqebah M., Jawarneh S., AlShaikh M., Al Smadi A., Alghamdi F.A., Alqurni J. S., & Alfagham H. Intrusion Detection Using an Improved Cuckoo Search Optimization Algorithm // Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. – 2024. – Vol. 15. – No. 2. – P. 73–93. https://doi.org/10.58346/JOWUA.2024.I2.006.

13. Ali E.S., Abd Elazim S.M., Abdelaziz A.Y. Ant Lion Optimization Algorithm for optimal location and sizing of renewable distributed generations // Renewable Energy. – 2017. – Vol. 101. – P. 1311–1324.

14. Debbarma S., Saikia L.C., Sinha N. Solution to automatic generation control problem using firefly algorithm optimized IλDμ controller // ISA Transactions. – 2014. – Vol. 53. – Issue 2. – P. 358–366.

15. Guha D., Roy P.K., Banerjee S. Optimal tuning of 3 degree-of-freedom proportional-integralderivative controller for hybrid distributed power system using dragonfly algorithm // Computers and Electrical Engineering. – 2018. – Vol. 72. –P. 137–153.

16. Mohamed M.J., Oleiwi B.K., Azar A.T., Hameed I.A. Coot optimization algorithm-tuned neural network-enhanced PID controllers for robust trajectory tracking of three-link rigid robot manipulator // Heliyon. – 2024. – Vol. 10. Article e32661.

17. Saravanan G., Suresh K.P., Pazhanimuthu C., Kumar R.S. Artificial rabbits optimization algorithm based tuning of PID controller parameters for improving voltage profile in AVR system using IoT // e-Prime – Advances in Electrical Engineering, Electronics and Energy. – 2024. – Vol. 8. Article 100523.

18. Mosaad A. M., Attia M. A., Abdelaziz A. Y. Whale optimization algorithm to tune PID and PIDA controllers on AVR system // Ain Shams Engineering Journal. – 2019. – Vol. 10. – P. 755–767.

19. Mohamed M.J., Oleiwi B.K., Azar A.T., Hameed I.A. Coot optimization algorithm-tuned neural network-enhanced PID controllers for robust trajectory tracking of three-link rigid robot manipulator // Heliyon. – 2024. – Vol. 10. Article e32661.

20. Güven A.F., Mengi O.Ö., Elseify M.A., & Kamel, S. Comprehensive Optimization of PID Controller Parameters for DC Motor Speed Management Using a Modified Jellyfish Search Algorithm // Optimal Control Applications and Methods. – 2024. – Vol. 0. – P. 1–23.

21. Güven A.F., & Mengi O.Ö. Nature-inspired algorithms for optimizing fractional order PID controllers in time-delayed systems // Optimal Control Applications and Methods. – 2024. – Vol. 45. – No. 3. – P. 1251–1279.

22. Vanchinathan K., Selvaganesan N. Adaptive fractional order PID controller tuning for brushless DC motor using Artificial Bee Colony algorithm // Results in Control and Optimization. – 2021. – Vol. 4. – P. 100032.

23. Sharif Naser Makhadmeh, Mohammed Azmi Al-Betar, Iyad Abu Doush, Mohammed A. Awadallah, Sofian Kassaymeh, Seyedali Mirjalili, Raed Abu Zitar. Recent advances in Grey Wolf Optimizer, its versions and applications: Review // IEEE Access. – 2023.

24. Hui T., Zeng W., Yu T. Core power control of the ADS based on genetic algorithm tuning PID controller // Nuclear Engineering and Design. – 2020. – Vol. 370. – P. 110835.

25. Izci D., Ekinci S., Hedley J., Demirören A. HHO Algorithm Based PID Controller Design for Aircraft Pitch Angle Control System // Proceedings of the 2020 International Conference on Advances in Computing, Communication, and Automation (ICACCA). Piscataway, NJ: IEEE. – 2020. – P. 9152897.

26. Serdar Ekinci, Davut Izci, Baran Hekimoğlu. Henry Gas Solubility Optimization Algorithm Based FOPID Controller Design for Automatic Voltage Regulator // Proceedings of the 2nd International Conference on Electrical, Communication and Computer Engineering (ICECCE). Istanbul, Turkey: IEEE, 2020. – P. 1–6.

27. Patil S.R., Agashe S. RelaxC controller and PID for time delay systems: Experimental test results on Boiler pilot plant // Results in Control and Optimization. – 2024. – Vol. 9. Article 100297.

28. Murugesan D., Jagatheesan K., Shah P., Sekhar R. Fractional order PIλDμ controller for microgrid power system using cohort intelligence optimization // Results in Control and Optimization. – 2023. – Vol. 11. Article 100218.

29. Ataşlar-Ayyıldız B. Robust Trajectory Tracking Control for Serial Robotic Manipulators Using Fractional Order-Based PTID Controller // Fractal Fract. – 2023. – Vol. 7. – Issue 3. – P. 250.