Metaheuristic Algorithm–Based Parameter Estimation for Permanent Magnet DC Motors

Abstract

Accurate parameter estimation of permanent magnet DC motors (PMDC) is essential for enhancing the required performance, stability and reliability in modern control systems. To establish a precise dynamic model of a PMDC motor, seven primary parameters must be accurately identified. This study presents a comprehensive comparative analysis of four metaheuristic optimization algorithms for PMDC parameter estimation: original particle swarm optimization (PSO), modified PSO incorporating constriction factor, hybrid PSO integrated with least squares method (LSM) and ant colony optimization (ACO). The algorithms are evaluated based on experimental current and speed response data. Results demonstrate that the proposed hybrid PSO–LSM approach achieves the best accuracy by synergistically combining global exploration with local refinement capabilities. Modified PSO ranks second, exhibiting accuracy comparable to ACO while demonstrating significantly faster computational efficiency. ACO delivers competitive estimation results but suffers from slower convergence rates. Original PSO provides the fastest computation time although its accuracy is lower. All algorithms maintain RMSE below 1%, confirming their effectiveness for PMDC parameter estimation. The findings indicate that hybrid PSO–LSM offers superior estimation accuracy and robustness. However, with a convergence time of approximately 5.2 min, the method is not intended for real-time adaptive control but is highly suitable for offline high-precision motor identification and initial commissioning applications.