Transfer function modeling and optimization speed response of bldc motor e-bike using intelligent controller

The applications of electric bikes (e-bikes) as the alternative vehicles are growing significantly in recent years. Brushless Direct Current (BLDC) motor is an essential component in an e-bike, which is used to actuating element of the e-bike. Besides that, to get the best performance from e-bike, BLDC motor speed control is significant. This research aims to compare the optimal transient response on a BLDC motor speed control system that is optimized using a combination of intelligent control. Intelligent control compared in this research is a combination of fuzzy logic with Proportional Integral Derivative (PID), which is optimized by Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). Transient responses measured are rise time, settling time, overshoot, and Integral Time Absolute Error (ITAE). The method used in this study consists of two parts: The first is mathematical modeling a BLDC motor in the form of a transfer function equation through system identification and the second is the tuning of PID controller parameters using PSO, FA, and hybrid PSO or FA. From the result of experiments and simulation, it is observed that hybrid fuzzy PID based on the firefly algorithm provides the best performance compared to other scenarios.