Artificial Intelligence-Driven Fuzzy Logic Control for Electrical Machines: A Systematic Review, Comparative Analysis, and Future Perspectives

Abstract

The rapid development of artificial intelligence (AI) has created new opportunities for improving the performance, robustness, and efficiency of electrical machine drive systems. Among AI-based approaches, fuzzy logic control (FLC) has attracted considerable attention because of its ability to handle nonlinear dynamics, parameter uncertainties, and external disturbances without relying on an accurate mathematical model. This review systematically examines FLC-based control strategies for electrical machine drives, with particular emphasis on induction motors, switched reluctance motors, permanent-magnet synchronous motors, synchronous reluctance motors, and brushless DC motors. The review follows the PRISMA 2020 framework, and the selected studies are analyzed according to machine type, FLC architecture, control strategy, optimization method, implementation platform, and validation approach. The reviewed evidence indicates that FLC-based strategies can improve dynamic response, tracking accuracy, robustness, and torque regulation under the specific conditions reported in the literature. Hybrid approaches combining FLC with field-oriented control, direct torque control, sliding-mode control, model predictive control, neural networks, ANFIS, and optimization algorithms provide additional opportunities for adaptation and parameter tuning. However, the reported performance is strongly dependent on machine topology, controller architecture, tuning methodology, computational requirements, and validation platform. The review also identifies important limitations, including the lack of standardized benchmarking, computational complexity, dependence on expert knowledge, and limited HIL and experimental validation of several advanced approaches. Emerging directions include Type-2 and higher-order fuzzy systems, neuro-fuzzy and hybrid AI controllers, data-driven optimization, digital-twin-assisted control, edge computing, and hardware-oriented implementation. The objective of this review is to provide a structured and critical synthesis of the existing evidence, clarify the evolution and practical applicability of AI-driven FLC approaches, and identify research priorities for reliable, computationally efficient, and experimentally validated electrical machine control.