Parameter estimation of permanent magnet DC motor using various particle swarm optimization variants
Year: 2026
Type: article
Abstract: DC motors have a crucial role in various industrial applications due to their flexibility and ability to deliver precise control. Accurate parameter estimation is a significant challenge to ensure optimal performance. This research investigates the estimation of seven parameters of DC motors using six variants of the Particle Swarm Optimization (PSO) algorithm, namely Hybrid Firefly and PSO (HFPSO), Combination of Limiting Factors and Inertial Weights-PSO (CFIW-PSO), Time-Varying Acceleration Coefficients-PSO (TVAC-PSO), Constricted Factor-PSO (CF-PSO), Adaptive Inertia Weight-PSO (AIW-PSO), and Standard-PSO. 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 exam the effectiveness 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 HFPSO provides the best estimation results with high accuracy and minimal deviation compared to other variants. CFIW-PSO and TVAC-PSO also show performance close to HFPSO, 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.
Source: Array
Authors: Po-Wen Hsueh, Mohamad Farid Fazdi
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,680
OpenAlex ID: W7153820458