INTELLIGENT ELECTRIC DRIVE CONTROL SYSTEMS AND ARTIFICIAL INTELLIGENCE: AN IN-DEPTH REVIEW
Keywords:
Intelligent electric drive control systems, artificial intelligence, machine learning, deep learning, neural networks, fuzzy logic control, reinforcement learning, predictive maintenance, edge computing, autonomous systems.Abstract
This paper presents a comprehensive review of intelligent electric drive control systems (IEDCS) and the role of artificial intelligence (AI) in their development and optimization. As the demand for more efficient, reliable, and intelligent electric drives grows, integrating AI techniques such as machine learning, neural networks, and fuzzy logic has emerged as a transformative solution. These advancements not only enhance control accuracy but also enable predictive maintenance, fault detection, and adaptive control, leading to improved performance in various industrial and automotive applications. This review explores the evolution of IEDCS, highlighting key technologies, design methodologies, and AI-driven algorithms that have revolutionized electric drive systems. It also discusses current challenges, potential improvements, and future trends in the integration of AI with electric drive controls, offering insights into the next generation of intelligent systems.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.