Robust Parameter Identification and Control Modeling of Low-Cost Brushed DC Motors Using the Nelder-Mead Algorithm - Channareth Srun; Mengseu Pheng; Sovathana Um; Chivon Choeung; Seven Siren; Sros Nhek (Control Systems and Optimization Letters, 2026)
Brushed DC motors are widely used in next-generation automation systems due to their low complexity and ease of control. However, more affordable models often lack sufficient information about their detailed parameters, which makes accurate control and modeling difficult. This paper presents an estimation method for the main parameters of a low-cost brushed DC motor using the Nelder-Mead algorithm. Real-time measurements of speed were obtained through Arduino-based testing, followed by parameter estimation using MATLAB and Simulink. The estimated parameters include armature resistance, inductance, moment of inertia, viscous damping coefficient, back electromotive force constant, and torque constant. The estimated results, validated strictly against a high-specification reference motor datasheet, demonstrate strong accuracy in critical mechanical parameters. Specifically, the algorithm estimated the torque constant with a minimal error of 0.17% and the viscous damping coefficient with an error of 4.8%. However, due to the inherent structural unidentifiability when relying solely on macroscopic speed measurements, electrical parameters such as armature resistance, inductance, and moment of inertia exhibited severe deviations ranging from 39.6% to 52.7%. While the objective function's inability to fully decouple these intertwined variables restricts isolated physical parameter extraction, the method effectively captures the equivalent macroscopic dynamic behavior. The predictive validity of the proposed method was further confirmed by implementing a PI controller based on the estimated transfer function. The experimental results confirm that despite internal physical parameter discrepancies, the algorithm provides an equivalent and robust dynamic model that significantly improves motor performance in control systems. This work proposes an inexpensive and efficient system identification solution for low-cost motor control characterization.