Performance Optimization of BLDC Motors for Fuzzy Lozic Controllers

Abstract

In place of traditional proportional-integral-derivative (PID) controllers, fuzzy logic controllers (FLCs) were utilized in this work to optimize the performance of brushless DC (BLDC) motors. A BLDC motor was modeled using MATLAB/Simulink as part of a simulation-based experimental strategy to assess the control techniques under different operating situations. A Mamdani-type inference system with two input variables—speed error and change in speed error—was used to develop the fuzzy logic controller. A Genetic Algorithm (GA) was then used to improve its membership functions. The fuzzy controller outperformed the PID, unoptimized FLC, and GA-optimized FLC in terms of lower rising time, settling time, steady-state error, torque ripple, and energy consumption, according to a comparative analysis. Furthermore, the motor's back-EMF waveform showed less total harmonic distortion (THD) and increased resistance to load disturbances thanks to the modified FLC. In practical applications, the results demonstrated how intelligent, adaptive control systems may improve the dynamic performance, stability, and efficiency of BLDC motor drives.