This research presents a comprehensive simulation-based evaluation of advanced intelligent control strategies for improving the dynamic performance of permanent magnet brushless DC (PMBLDC) motors under variable operating conditions. Traditional fixed-gain controllers often fail to adapt to system nonlinearities and real-time disturbances, prompting the need for more robust and adaptive control solutions. In this context, the study implements and systematically benchmarks multiple control algorithms including fuzzy logic control (FLC), adaptive neuro-fuzzy inference systems (ANFIS), and self-tuning PI controllers within a unified MATLAB/Simulink framework. The controllers are evaluated against critical performance metrics such as rise time, settling time, peak time, overshoot, and behavior under load fluctuations and speed reversals. Unlike existing literature, which typically examines these methods in isolation or limited scopes, this work offers a comparative, application-focused analysis of five control approaches using consistent modeling conditions. The findings demonstrate how intelligent control schemes can significantly enhance response precision and robustness, offering practical insights for motor drive design in industrial automation and energy-efficient electric propulsion systems.
Srivastava et al. (2026) studied this question.