Abstract
This study focuses on assessing the performance of a Proportional-Integral-Derivative (PID) controller integrated with an Adaptive Neuro-Fuzzy Inference System (ANFIS) in the context of speed regulation and harmonic reduction in Brushless DC (BLDC) motor applications. Rising BLDC motor speed elevates Total harmonic distortion (THD) due to non-linearity. THD reduction is vital for efficiency, reliability, and compliance in applications like electric vehicles, HVAC, and industrial automation, ensuring optimal performance and longevity. Through simulation-based design and implementation, the effectiveness of the ANFIS-PID controller is evaluated for achieving precise speed control and reducing harmonic distortions in a virtual environment. Various conventional control topologies are considered, with the ANFIS-PID controller demonstrating superior performance. The synergy of adaptive fuzzy logic and classic control components allows the ANFIS-PID controller to outperform others, particularly in dynamic conditions and varying motor characteristics, offering enhanced speed regulation and harmonic reduction in BLDC motor applications. Detailed simulations in MATLAB/Simulink software thoroughly assess the controller's dynamic response and its ability to accurately regulate BLDC motor speed while concurrently reducing harmonic distortions.
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