Fuzzy Logic and Neural Network Control of Electric Drives: Intelligent Approaches for Performance Enhancement

Abstract

Intelligent control techniques such as Fuzzy Logic Control (FLC) and Neural Network Control (NNC) have emerged as powerful tools to enhance the performance of electric drives. This paper investigates FLC and NNC-based drive control methods for DC, induction, and PMSM motors, focusing on speed regulation, torque control, and disturbance rejection. Simulation and experimental results demonstrate that intelligent controllers outperform classical PID controllers under non-linear and time-varying load conditions. FLC provides robustness against uncertainties, while NNC offers adaptive learning and predictive capabilities. The study highlights the potential of hybrid fuzzy-neural controllers for achieving high-performance, energy-efficient electric drives.

KEYWORDS: Fuzzy Logic Control, Neural Network Control, Electric Drives, Intelligent Control, Adaptive Control, Hybrid Controllers.