Comparative Analysis of PID, Self-Tunning PID, and Adaptive Neuro-Fuzzy Logic Inference System Controllers for BLDC Motor Speed Control

Brushless direct current motors (BLDCs) have become increasingly popular in the industry, particularly in applications such as two-wheeled vehicles and light-duty vehicles, especially in the courier and driving sectors. This research paper presents a comparative study of three speed controllers for regulating the rotational speed of BLDC motor: (i) proportional-integral derivative (PID), (ii) self-tuning proportional-integral derivative (STPID), and (iii) adaptive neuro-fuzzy logic inference system (ANFIS). In this paper, these three speed controllers are modeled and studied using MATLAB / Simulink. The simulation results compare key performance metrics, including rise time, percentage speed overshoots, and settling times of the rotational speed response at startup condition with various motor load are discussed. In addition, the starting rate and the rise time of the motor torques are examined.