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
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
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

Volume 8, Issue 3
Summer 2025
Pages 131-140

  • Receive Date 23 April 2025
  • Revise Date 08 July 2025
  • Accept Date 30 August 2025