DC Motor Parameter Identification Using Leveraging SOP and Particle SwarmOptimization Plates

Accurate DC motor parameter estimation iscrucial for enhancing control precision in robotics, electricvehicles, and industrial automation. This study introduces aStatic Optimization Problem combined with Particle SwarmOptimization (SOP-PSO) to address limitations in traditionalmethods, such as Weighted Recursive Least Squares(WRLS), Genetic Algorithm (GA), and Recursive LeastSquares (RLS). Using the SOP-PSO framework, criticalmotor parameters including armature resistance, inductance,inertia, magnetic flux, and damping—are estimated with highaccuracy and computational efficiency. Comparative resultsdemonstrate that SOP-PSO achieves faster convergence,greater noise resilience, and consistently low error rates(parameter deviations under ۲%), even under noisyconditions. Unlike GA, which requires large populations, andRLS, which is sensitive to tuning, SOP-PSO offers a robust,periodic estimation solution without needing continuous realtimeupdates. These findings validate SOP-PSO as a reliablealternative for precise DC motor parameter estimation, withapplications in high-demand control environments.

Keywords:

DC Motor Parameter Estimation , Particle SwarmOptimization (PSO) , Static Optimization Problem (SOP) , NoiseResilience , Control Precision.