Investigation of type 1 and type 2 fuzzy logic controllers performance: application of speed control of BLDC motor

Abstract

In this paper, a simulation study enhanced to model that the speed control of brushless direct current (BLDC) motors used in electric vehicles with intelligent control methods. The simulation study was prepared in Matlab/Simulink environment. The first control method is Type-1 fuzzy logic control (T1FLC), and the second control method is the Intermittent Type-2 fuzzy logic control (IT2FLC) model. Membership functions for different membership numbers have been created for both types of FLC models. These are 3×3, 5×5, 7×7. Control methods are prepared in Matlab/M-file environment. The model is defined as the input variable of the error, which is the difference between the reference speed and the motor speed, and the output variable of the Pulse Width Modulation (PWM) signal applied to the motor. The simulation study maintains the speed of the BLDC motor up to the reference speed with T1FLC and IT2FLC controllers, depending on the reference speed and applied load values. Depending on the number of different memberships, the effects of controller performances on the control of motor speed have been observed. The graphs and findings of the experiment are shown in the results and discussion section.

Simulation study of brushless DC motor speed control system based on GA-PID

“…Comparative analysis of its performance against traditional PID controllers, PSO-PI controllers, and ANFIS revealed that the hybrid GWO-PI controller exhibited superior efficiency in steady-state, variable load, and variable set speed scenarios. Karabacak Y [2] utilized Type-2 fuzzy logic control to model the brushless direct current motor and regenerative braking system in electric vehicles. Simulation results demonstrate the successful control of the regenerative braking system with this approach while maintaining braking quality.…”