Electrified urban mobility and automated logistics increasingly rely on compact, high-efficiency electric drives for electric vehicles, shared micromobility, and last-mile delivery/warehouse robots within sustainable, digitally managed transport ecosystems. Permanent-magnet brushless DC (PMBLDC) motors are attractive in these platforms due to their high efficiency and power density, yet precise speed regulation remains challenging under stop-and-go duty cycles, rapid load changes, nonlinear dynamics, parameter variations, and sensing/commutation uncertainties. This paper investigates PWM-based PMBLDC speed control and compares PID, fractional-order PID (FOPID), and adaptive neuro-fuzzy (ANFIS) controllers using a coupled electro-mechanical MATLAB/Simulink model and identical tracking tests evaluated by standard integral error indices (IAE, ISE, ITAE, ITSE). Results show that FOPID improves transient response and disturbance rejection relative to classical PID, while ANFIS performs competitively under nominal conditions but exhibits sensitivity to measurement noise in our implementation. These findings highlight robust motor-drive control particularly fractional-order designs, as an enabling component for reliable electrified mobility and responsive urban logistics
Shoeib et al. (2026) studied this question.