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Performance Optimization of PID Controllers for DC Machine Drives Using PSO, ACO, and Hybrid PSO-ACO Algorithms

Year 2025, Volume: 9 Issue: 1, 118 - 129, 30.06.2025

Ahmed Emin Yörük , Nuri Alper Metin , Murat Lüy

https://doi.org/10.47897/bilmes.1688286

Abstract

PID controllers are utilised extensively in the domain of electric motors and drives. The values of the PID controller have a direct impact on the controller's characteristics. Establishing optimal values is imperative to enhance the efficacy of control mechanisms. Consequently, a multitude of optimization algorithms have been developed. Employing these algorithms facilitates the optimisation of the controller's optimal values with greater efficiency, requiring less experience and a shorter timeframe. In this study, the parameters of the PID controller employed in the motor drive developed for a direct current (DC) motor are optimised by three distinct heuristic optimisation methods: The following optimization methods are used: Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), and PSO-ACO, which is a combination of these two methods. The execution of simulations is conducted within the MATLAB environment, with a subsequent comparative analysis of control performances. This study proposes a pioneering optimisation approach that integrates the PSO and ACO algorithms. The PID controller attains the reference value in the most efficient timeframe through this methodology. The simulation results show that the PSO-ACO method demonstrates optimal performance, followed by PSO and ACO.

Keywords

PID Control PSO ACO PSO-ACO DC Machine

References

  • H. O. Erkol, "GA ve PSO ile Kontrol Parametrelerinin Optimizasyonu," Karaelmas Fen ve Mühendislik Dergisi, vol. 7, no. 1, pp. 179–185, 2017.
  • J. G. Ziegler and N. B. Nichols, "Optimum Settings for Automatic Controllers," Journal of Dynamic Systems, Measurement and Control, vol. 115, no. 2B, pp. 759–765, 1993.
  • R. C. Panda, C. Yu, and H. Huang, "PID Tuning Rules for SOPDT Systems: Review and Some New Results," ISA Transactions, vol. 43, no. 2, pp. 283–295, 2004.
  • Ş. Yıldırım, M. S. Bingol, and S. Savaş, "Tuning PID controller parameters of the DC motor with PSO algorithm," International Review of Applied Sciences and Engineering, vol. 15, no. 3, pp. 281–286, 2024.
  • J. E. Oche, H. A. Bashir, and T. J. Shima, "PSO-optimized model reference adaptive PID controller for precise DC motor speed control," Nigerian Journal of Technological Development, vol. 21, no. 4, pp. 135–144, Dec. 2024, doi: 10.4314/njtd.v21i4.2473.
  • R. C. Beremeh et al., "A hybrid optimization scheme for tuning fractional order PID controller parameters for a DC motor," International Journal of Science and Research Archive, vol. 13, no. 2, pp. 2779–2789, 2024, doi: 10.30574/ijsra.2024.13.2.2291.
  • A. Najem, A. Moutabir, and A. Ouchatti, "Simulation and Arduino hardware implementation of ACO, PSO, and FPA optimization algorithms for speed control of a DC motor," International Journal of Robotics and Control Systems, vol. 4, no. 3, pp. 1186–1206, 2024, doi: 10.31763/ijrcs.v4i3.1483.
  • A. F. Güven, O. Ö. Mengi, M. A. Elseify, and S. Kamel, "Comprehensive optimization of PID controller parameters for DC motor speed management using a modified jellyfish search algorithm," Optimal Control Applications and Methods, vol. 46, no. 1, pp. 320–342, Jan. 2025, doi: 10.1002/oca.3218.
  • S. Ekinci et al., "Advanced control parameter optimization in DC motors and liquid level systems," Scientific Reports, vol. 15, no. 1394, Jan. 2025, Doi: 10.1038/s41598-025-85273-y.
  • M. Moghaddas, M. R. Dastranj, N. Changizi, and M. Rouhani, "PID Control of DC Motor Using Particle Swarm Optimization (PSO) Algorithm," Journal of Mathematics and Computer Science, vol. 1, no. 4, pp. 386–391, Dec. 2010, doi: 10.22436/jmcs.001.04.16.
  • K. J. Åström and T. Hägglund, "The Future of PID Control," Control Engineering Practice, vol. 9, no. 11, pp. 1163–1175, Nov. 2001, doi: 10.1016/S0967-0661(01)00062-4.
  • G. Beni and J. Wang, "Swarm Intelligence in Cellular Robotic Systems," Proceedings of NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, Jun. 1989.
  • P. Erdoğmuş, "Doğadan Esinlenen Optimizasyon Algoritmaları ve Optimizasyon Algoritmalarının Optimizasyonu," DÜBİTED, vol. 4, no. 1, pp. 293–304, 2016.
  • J. M. Bishop, "Stochastic Searching Networks," Proc. 1st IEE Int. Conf. on Artificial Neural Networks, pp. 329–331, London, UK, 1989.
  • M. Dorigo and T. Stützle, Ant Colony Optimization, Cambridge, MA: MIT Press, 2004.
  • K. E. Parsopoulos and M. N. Vrahatis, "Recent Approaches to Global Optimization Problems Through Particle Swarm Optimization," Natural Computing, vol. 1, no. 2–3, pp. 235–306, 2002, doi: 10.1023/A:1016568309421.
  • J. Holland, "Genetic Algorithms," Scientific American Journal, 1992, pp. 66–72.
  • R. Storn and K. Price, "Differential Evolution — A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces," Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997.
  • T. Stützle and H. H. Hoos, "MAX–MIN Ant System," 1999.
  • M. Dorigo, G. Di Caro, and L. M. Gambardella, "Ant algorithms for discrete optimization," Artificial Life, vol. 5, no. 2, pp. 137–172, 1999.
  • M. Dorigo and T. Stützle, Ant Colony Optimization, Cambridge, MA: MIT Press, 2004.
  • M. Dorigo, M. Birattari, and T. Stützle, "Ant Colony Optimization," IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28–39, 2006, doi: 10.1109/MCI.2006.329691.
  • D. Sandoval, I. Soto, and P. Adasme, "Control of direct current motor using Ant Colony optimization," 2015 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), Santiago, Chile, 2015, pp. 79–82, doi: 10.1109/Chilecon.2015.7400356.
  • F. Ulu, G. Tabansız Göç, and F. Çavdur, "Floyd-Warshall ve Karınca Kolonisi Optimizasyonu Algoritmaları ile Depo Rota Planlaması," Verimlilik Dergisi, vol. 59, no. 2, pp. 337–354, 2025. [Online]. Available: https://doi.org/10.51551/verimlilik.1539618.
  • F. Marini and B. Walczak, "Particle Swarm Optimization (PSO): A Tutorial," Chemometrics and Intelligent Laboratory Systems, vol. 149, pp. 153–165, 2015.
  • C. A. Coello Coello and M. Reyes-Sierra, "Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art," International Journal of Computational Intelligence Systems, vol. 2, no. 3, pp. 287–306, 2006.
  • M. Lüy and N. A. Metin, "PID Control Medium Size Wind Turbine Control with Integrated Blade Pitch Angle," Internatıonal Scıentıfıc And Vocatıonal Journal (Isvos Journal), vol. 6, no. 1, pp. 22-31, Jun. 2022. [Online]. Available: https://doi.org/10.47897/bilmes.1091968
  • D. Wang, D. Tan, and L. Liu, "Particle Swarm Optimization Algorithm: An Overview," Soft Computing, vol. 22, no. 2, pp. 387–408, 2018, doi: 10.1007/s00500-017-2534-6.
  • A. T. Kiani et al., "An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters," Energies, vol. 14, no. 11, p. 2980, 2021, doi: 10.3390/en14112980.
  • B. Nagaraj and P. Vijayakumar, "A Comparative Study of PID Controller Tuning Using GA, EP, PSO, and ACO," Journal of Automation, Mobile Robotics & Intelligent Systems, vol. 5, no. 2, pp. 42–48, 2011.
  • P. Thakur, J. Mishra, C. Yadav, and K. Tiwari, "PSO–ACO PID Techniques for Stability Enhancement," International Journal of Novel Research and Development (IJNRD), vol. 3, no. 6, pp. 75–80, Jun. 2018.
  • N. S. R. Krishnan and D. Devaraj, "Comparison of Bees Algorithm, Ant Colony Optimisation, and Particle Swarm Optimisation for PID Controller Tuning," Proc. 11th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 1975–1980, 2009, doi: 10.1145/1500879.1500912.
  • M. R. H. Mojumder and N. K. Roy, "Review of Meta-Heuristic Optimization Algorithms to Tune the PID Controller Parameters for Automatic Voltage Regulator," arXiv preprint, arXiv:2409.00538, Aug. 2024.
  • A. Yıldız, M. E. Karslıoğlu, and S. Yıldız, "Effects of objective function in PID controller design for an AVR system," Journal of Engineering Sciences and Design, vol. 8, no. 2, pp. 398–407, 2020.
  • A. A. El-Gammal and A. A. El-Samahy, "A Modified Design of PID Controller for DC Motor Drives Using Particle Swarm Optimization," International Conference on Power Engineering, Energy and Electrical Drives, pp. 419–424, Lisbon, 2009.
  • "DC Motor Speed: System Modeling," Control Tutorials for MATLAB and Simulink, University of Michigan. [Online]. Available: https://ctms.engin.umich.edu/CTMS/index.php?example=MotorSpeed&section=SystemModeling.

Performance Optimization of PID Controllers for DC Machine Drives Using PSO, ACO, and Hybrid PSO-ACO Algorithms

Year 2025, Volume: 9 Issue: 1, 118 - 129, 30.06.2025

Ahmed Emin Yörük , Nuri Alper Metin , Murat Lüy

https://doi.org/10.47897/bilmes.1688286

Abstract

PID controllers are utilised extensively in the domain of electric motors and drives. The values of the PID controller have a direct impact on the controller's characteristics. Establishing optimal values is imperative to enhance the efficacy of control mechanisms. Consequently, a multitude of optimization algorithms have been developed. Employing these algorithms facilitates the optimisation of the controller's optimal values with greater efficiency, requiring less experience and a shorter timeframe. In this study, the parameters of the PID controller employed in the motor drive developed for a direct current (DC) motor are optimised by three distinct heuristic optimisation methods: The following optimization methods are used: Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), and PSO-ACO, which is a combination of these two methods. The execution of simulations is conducted within the MATLAB environment, with a subsequent comparative analysis of control performances. This study proposes a pioneering optimisation approach that integrates the PSO and ACO algorithms. The PID controller attains the reference value in the most efficient timeframe through this methodology. The simulation results show that the PSO-ACO method demonstrates optimal performance, followed by PSO and ACO.

Keywords

PID Control PSO ACO PSO-ACO DC Machine

References

  • H. O. Erkol, "GA ve PSO ile Kontrol Parametrelerinin Optimizasyonu," Karaelmas Fen ve Mühendislik Dergisi, vol. 7, no. 1, pp. 179–185, 2017.
  • J. G. Ziegler and N. B. Nichols, "Optimum Settings for Automatic Controllers," Journal of Dynamic Systems, Measurement and Control, vol. 115, no. 2B, pp. 759–765, 1993.
  • R. C. Panda, C. Yu, and H. Huang, "PID Tuning Rules for SOPDT Systems: Review and Some New Results," ISA Transactions, vol. 43, no. 2, pp. 283–295, 2004.
  • Ş. Yıldırım, M. S. Bingol, and S. Savaş, "Tuning PID controller parameters of the DC motor with PSO algorithm," International Review of Applied Sciences and Engineering, vol. 15, no. 3, pp. 281–286, 2024.
  • J. E. Oche, H. A. Bashir, and T. J. Shima, "PSO-optimized model reference adaptive PID controller for precise DC motor speed control," Nigerian Journal of Technological Development, vol. 21, no. 4, pp. 135–144, Dec. 2024, doi: 10.4314/njtd.v21i4.2473.
  • R. C. Beremeh et al., "A hybrid optimization scheme for tuning fractional order PID controller parameters for a DC motor," International Journal of Science and Research Archive, vol. 13, no. 2, pp. 2779–2789, 2024, doi: 10.30574/ijsra.2024.13.2.2291.
  • A. Najem, A. Moutabir, and A. Ouchatti, "Simulation and Arduino hardware implementation of ACO, PSO, and FPA optimization algorithms for speed control of a DC motor," International Journal of Robotics and Control Systems, vol. 4, no. 3, pp. 1186–1206, 2024, doi: 10.31763/ijrcs.v4i3.1483.
  • A. F. Güven, O. Ö. Mengi, M. A. Elseify, and S. Kamel, "Comprehensive optimization of PID controller parameters for DC motor speed management using a modified jellyfish search algorithm," Optimal Control Applications and Methods, vol. 46, no. 1, pp. 320–342, Jan. 2025, doi: 10.1002/oca.3218.
  • S. Ekinci et al., "Advanced control parameter optimization in DC motors and liquid level systems," Scientific Reports, vol. 15, no. 1394, Jan. 2025, Doi: 10.1038/s41598-025-85273-y.
  • M. Moghaddas, M. R. Dastranj, N. Changizi, and M. Rouhani, "PID Control of DC Motor Using Particle Swarm Optimization (PSO) Algorithm," Journal of Mathematics and Computer Science, vol. 1, no. 4, pp. 386–391, Dec. 2010, doi: 10.22436/jmcs.001.04.16.
  • K. J. Åström and T. Hägglund, "The Future of PID Control," Control Engineering Practice, vol. 9, no. 11, pp. 1163–1175, Nov. 2001, doi: 10.1016/S0967-0661(01)00062-4.
  • G. Beni and J. Wang, "Swarm Intelligence in Cellular Robotic Systems," Proceedings of NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, Jun. 1989.
  • P. Erdoğmuş, "Doğadan Esinlenen Optimizasyon Algoritmaları ve Optimizasyon Algoritmalarının Optimizasyonu," DÜBİTED, vol. 4, no. 1, pp. 293–304, 2016.
  • J. M. Bishop, "Stochastic Searching Networks," Proc. 1st IEE Int. Conf. on Artificial Neural Networks, pp. 329–331, London, UK, 1989.
  • M. Dorigo and T. Stützle, Ant Colony Optimization, Cambridge, MA: MIT Press, 2004.
  • K. E. Parsopoulos and M. N. Vrahatis, "Recent Approaches to Global Optimization Problems Through Particle Swarm Optimization," Natural Computing, vol. 1, no. 2–3, pp. 235–306, 2002, doi: 10.1023/A:1016568309421.
  • J. Holland, "Genetic Algorithms," Scientific American Journal, 1992, pp. 66–72.
  • R. Storn and K. Price, "Differential Evolution — A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces," Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997.
  • T. Stützle and H. H. Hoos, "MAX–MIN Ant System," 1999.
  • M. Dorigo, G. Di Caro, and L. M. Gambardella, "Ant algorithms for discrete optimization," Artificial Life, vol. 5, no. 2, pp. 137–172, 1999.
  • M. Dorigo and T. Stützle, Ant Colony Optimization, Cambridge, MA: MIT Press, 2004.
  • M. Dorigo, M. Birattari, and T. Stützle, "Ant Colony Optimization," IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28–39, 2006, doi: 10.1109/MCI.2006.329691.
  • D. Sandoval, I. Soto, and P. Adasme, "Control of direct current motor using Ant Colony optimization," 2015 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), Santiago, Chile, 2015, pp. 79–82, doi: 10.1109/Chilecon.2015.7400356.
  • F. Ulu, G. Tabansız Göç, and F. Çavdur, "Floyd-Warshall ve Karınca Kolonisi Optimizasyonu Algoritmaları ile Depo Rota Planlaması," Verimlilik Dergisi, vol. 59, no. 2, pp. 337–354, 2025. [Online]. Available: https://doi.org/10.51551/verimlilik.1539618.
  • F. Marini and B. Walczak, "Particle Swarm Optimization (PSO): A Tutorial," Chemometrics and Intelligent Laboratory Systems, vol. 149, pp. 153–165, 2015.
  • C. A. Coello Coello and M. Reyes-Sierra, "Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art," International Journal of Computational Intelligence Systems, vol. 2, no. 3, pp. 287–306, 2006.
  • M. Lüy and N. A. Metin, "PID Control Medium Size Wind Turbine Control with Integrated Blade Pitch Angle," Internatıonal Scıentıfıc And Vocatıonal Journal (Isvos Journal), vol. 6, no. 1, pp. 22-31, Jun. 2022. [Online]. Available: https://doi.org/10.47897/bilmes.1091968
  • D. Wang, D. Tan, and L. Liu, "Particle Swarm Optimization Algorithm: An Overview," Soft Computing, vol. 22, no. 2, pp. 387–408, 2018, doi: 10.1007/s00500-017-2534-6.
  • A. T. Kiani et al., "An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters," Energies, vol. 14, no. 11, p. 2980, 2021, doi: 10.3390/en14112980.
  • B. Nagaraj and P. Vijayakumar, "A Comparative Study of PID Controller Tuning Using GA, EP, PSO, and ACO," Journal of Automation, Mobile Robotics & Intelligent Systems, vol. 5, no. 2, pp. 42–48, 2011.
  • P. Thakur, J. Mishra, C. Yadav, and K. Tiwari, "PSO–ACO PID Techniques for Stability Enhancement," International Journal of Novel Research and Development (IJNRD), vol. 3, no. 6, pp. 75–80, Jun. 2018.
  • N. S. R. Krishnan and D. Devaraj, "Comparison of Bees Algorithm, Ant Colony Optimisation, and Particle Swarm Optimisation for PID Controller Tuning," Proc. 11th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 1975–1980, 2009, doi: 10.1145/1500879.1500912.
  • M. R. H. Mojumder and N. K. Roy, "Review of Meta-Heuristic Optimization Algorithms to Tune the PID Controller Parameters for Automatic Voltage Regulator," arXiv preprint, arXiv:2409.00538, Aug. 2024.
  • A. Yıldız, M. E. Karslıoğlu, and S. Yıldız, "Effects of objective function in PID controller design for an AVR system," Journal of Engineering Sciences and Design, vol. 8, no. 2, pp. 398–407, 2020.
  • A. A. El-Gammal and A. A. El-Samahy, "A Modified Design of PID Controller for DC Motor Drives Using Particle Swarm Optimization," International Conference on Power Engineering, Energy and Electrical Drives, pp. 419–424, Lisbon, 2009.
  • "DC Motor Speed: System Modeling," Control Tutorials for MATLAB and Simulink, University of Michigan. [Online]. Available: https://ctms.engin.umich.edu/CTMS/index.php?example=MotorSpeed&section=SystemModeling.
There are 36 citations in total.

Details

Primary Language English
Subjects Electrical Machines and Drives, Electronics, Electronic Device and System Performance Evaluation, Testing and Simulation, Power Electronics, Electronics, Sensors and Digital Hardware (Other)
Journal Section Articles
Authors

Ahmed Emin Yörük 0000-0002-1372-1734

Nuri Alper Metin 0000-0002-9962-917X

Murat Lüy 0000-0002-2378-0009

Publication Date June 30, 2025
Submission Date May 2, 2025
Acceptance Date June 23, 2025
Published in Issue Year 2025 Volume: 9 Issue: 1

Cite

APA Yörük, A. E., Metin, N. A., & Lüy, M. (2025). Performance Optimization of PID Controllers for DC Machine Drives Using PSO, ACO, and Hybrid PSO-ACO Algorithms. International Scientific and Vocational Studies Journal, 9(1), 118-129. https://doi.org/10.47897/bilmes.1688286
AMA Yörük AE, Metin NA, Lüy M. Performance Optimization of PID Controllers for DC Machine Drives Using PSO, ACO, and Hybrid PSO-ACO Algorithms. ISVOS. June 2025;9(1):118-129. doi:10.47897/bilmes.1688286
Chicago Yörük, Ahmed Emin, Nuri Alper Metin, and Murat Lüy. “Performance Optimization of PID Controllers for DC Machine Drives Using PSO, ACO, and Hybrid PSO-ACO Algorithms”. International Scientific and Vocational Studies Journal 9, no. 1 (June 2025): 118-29. https://doi.org/10.47897/bilmes.1688286.
EndNote Yörük AE, Metin NA, Lüy M (June 1, 2025) Performance Optimization of PID Controllers for DC Machine Drives Using PSO, ACO, and Hybrid PSO-ACO Algorithms. International Scientific and Vocational Studies Journal 9 1 118–129.
IEEE A. E. Yörük, N. A. Metin, and M. Lüy, “Performance Optimization of PID Controllers for DC Machine Drives Using PSO, ACO, and Hybrid PSO-ACO Algorithms”, ISVOS, vol. 9, no. 1, pp. 118–129, 2025, doi: 10.47897/bilmes.1688286.
ISNAD Yörük, Ahmed Emin et al. “Performance Optimization of PID Controllers for DC Machine Drives Using PSO, ACO, and Hybrid PSO-ACO Algorithms”. International Scientific and Vocational Studies Journal 9/1 (June2025), 118-129. https://doi.org/10.47897/bilmes.1688286.
JAMA Yörük AE, Metin NA, Lüy M. Performance Optimization of PID Controllers for DC Machine Drives Using PSO, ACO, and Hybrid PSO-ACO Algorithms. ISVOS. 2025;9:118–129.
MLA Yörük, Ahmed Emin et al. “Performance Optimization of PID Controllers for DC Machine Drives Using PSO, ACO, and Hybrid PSO-ACO Algorithms”. International Scientific and Vocational Studies Journal, vol. 9, no. 1, 2025, pp. 118-29, doi:10.47897/bilmes.1688286.
Vancouver Yörük AE, Metin NA, Lüy M. Performance Optimization of PID Controllers for DC Machine Drives Using PSO, ACO, and Hybrid PSO-ACO Algorithms. ISVOS. 2025;9(1):118-29.


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