Transactions on Machine Intelligence

Transactions on Machine Intelligence

Speed Control Optimization of Permanent Magnet Synchronous Motors (PMSM) Using the Cuckoo Optimization Algorithm (COA)

Document Type : Original Article

Authors
M. Moradi 1
M. Moghadasian 2
1 Department of Electrical and Electronics Engineering, Shuhada-e-Hoveyzeh University of Technology, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 Assistant Professor, Department of Electrical and Electronics Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
10.22034/tmi.2025.244861
Abstract
This paper presents the speed control optimization of a Permanent Magnet Synchronous Motor (PMSM) utilizing the Cuckoo Optimization Algorithm (COA). Due to their high-power density, superior efficiency, and robust structural design, PMSMs are extensively utilized in a wide range of industrial and domestic applications. However, achieving precise and optimal speed control in these motors remains a significant challenge due to their non-linear dynamics. In this research, the parameters of a Proportional-Integral-Derivative (PID) controller are optimized to minimize speed tracking errors and enhance the motor's dynamic response. The Cuckoo Optimization Algorithm (COA) was selected for this optimization task owing to its algorithmic simplicity, fast convergence, and high efficiency in solving complex engineering problems. The problem formulation incorporates an objective function designed based on the Integral Squared Error (ISE) criterion, where the decision variables are defined as the tuning gains of the PID controller. Simulation studies conducted in MATLAB/Simulink R2024a demonstrate that employing the COA for PID parameter tuning yields a substantial improvement in the performance of the PMSM drive system. Specifically, a significant reduction in speed error, suppressed overshoot, and an accelerated system settling time are achieved using the proposed optimization approach. Finally, the proposed method is benchmarked against other metaheuristic optimization techniques, confirming its superior performance and efficacy.
Keywords
Cuckoo Optimization Algorithm (COA)
Speed Control
Permanent Magnet Synchronous Motor (PMSM)
PID Controller
Error Minimization
Simulation
Dynamic Response
[1]      Pillay, P., & Krishnan, R. (1989). Modeling, simulation, and analysis of permanent-magnet motor drives, Part I: The permanent-magnet synchronous motor drive. IEEE Transactions on Industry Applications, 25(2), 265–273. https://doi.org/10.1109/28.25541
[2]      Pillay, P., & Krishnan, R. (1989). Modeling, simulation, and analysis of permanent-magnet motor drives, Part II: The brushless DC motor drive. IEEE Transactions on Industry Applications, 25(2), 274–279. https://doi.org/10.1109/28.25542
[3]      Krishnan, R. (2010). Permanent magnet synchronous and brushless DC motor drives. CRC Press. https://doi.org/10.1201/9781420014235
[4]      Jahns, T. M., Kliman, G. B., & Neumann, T. W. (1986). Interior permanent-magnet synchronous motors for adjustable-speed drives. IEEE Transactions on Industry Applications, IA-22(4), 738–747. https://doi.org/10.1109/TIA.1986.4504786
[5]      Morimoto, S., Sanada, M., & Takeda, Y. (1994). Wide-speed operation of interior permanent magnet synchronous motors with high-performance current regulator. IEEE Transactions on Industry Applications, 30(4), 920–926. https://doi.org/10.1109/28.297908
[6]      Vas, P. (1998). Sensorless vector and direct torque control. Oxford University Press.
[7]      Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95—International Conference on Neural Networks (Vol. 4, pp. 1942–1948). IEEE. https://doi.org/10.1109/ICNN.1995.488968
[8]      Storn, R., & Price, K. (1997). Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359. https://doi.org/10.1023/A:1008202821328
[9]      Yang, X.-S. (2010). Engineering optimization: An introduction with metaheuristic applications. John Wiley & Sons.
[10]   Rajabioun, R. (2011). Cuckoo optimization algorithm. Applied Soft Computing, 11(8), 5508–5518. https://doi.org/10.1016/j.asoc.2011.05.008
[11]   Jafari, S., Bozorg-Haddad, O., & Chu, X. (2017). Cuckoo optimization algorithm (COA). In O. Bozorg-Haddad (Ed.), Advanced optimization by nature-inspired algorithms (pp. 39–49). Springer. https://doi.org/10.1007/978-981-10-5221-7_4
[12]   Magnet, S. (2013). Mathematical modeling and simulation of permanent magnet synchronous motor. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3720–3726.
[13]   Saleh, S. A. (2016). The formulation of a power flow using dq reference frame components—Part I: Balanced 3-phase systems. IEEE Transactions on Industry Applications, 52(5), 3682–3693. https://doi.org/10.1109/TIA.2016.2582827
Transactions on Machine Intelligence
Volume 8, Issue 3
Summer 2025
Pages 156-168

  • Receive Date 23 April 2025
  • Revise Date 08 July 2025
  • Accept Date 02 August 2025