Recent Trends in Electrical Machines and Drives ISSN: 3139-2067 (Online)

Abstract

Electric drives are the backbone of modern industrial automation, renewable energy systems, electric vehicles, aerospace actuators, and robotics. Traditional control strategies such as PID and linear state feedback have dominated the field for decades. However, with growing demands for high dynamic performance, robustness under uncertainty, and adaptation to parameter variations, artificial intelligence (AI)-based adaptive control strategies have emerged as a promising alternative. This paper explores the integration of neural networks (NN) and fuzzy logic systems (FLS) into adaptive controllers for electric drives, offering insights into architecture, learning algorithms, stability analysis, and real-world performance metrics. Detailed comparisons, case studies, and simulation results are provided to illustrate the advantages and limitations of these approaches.

Keywords: Electric drives, adaptive control, neural networks, fuzzy logic, robustness, parameter uncertainty, AI control, induction motor, permanent magnet synchronous motor (PMSM).