Parameter estimation of permanent magnet DC motor using various particle swarm optimization variants

This study proposes the estimation of seven parameters of DC motors using six Particle Swarm Optimization variants including a hybrid PSO approach. PSO was selected due to its efficacy as a metaheuristic algorithm in addressing the issue of identifying the best solution in a large and non-linear space, which frequently occurs in the estimation of DC motor parameters. Different variants of PSO were selected to examine the impact of various modifications in the solution search mechanism aimed at addressing specific challenges in DC motor parameter estimation. A test signal in the form of a sinusoidal wave with a frequency of 10 Hz is used to evaluate the accuracy and response of the algorithm to dynamic conditions. The results show that Hybrid PSO and Firefly provides the best estimation results with high accuracy and minimal deviation compared to other variants. Constricted Factor and Inertia Weight PSO and Time Varying Acceleration Coefficients PSO also show performance close to Hybrid PSO and Firefly, while Standard-PSO has the lowest performance, although still within acceptable accuracy limits. The most significant estimation error only reaches 1.73%, which is still below the 5% threshold.