Integration of Artificial Intelligence in the Control, Diagnosis Faults, and Estimation of Parameters of Permanent Magnet Synchronous Machines (PMSMs)

Abstract

Permanent Magnet Synchronous Machines (PMSMs) are widely used in modern industrial applications due to their high efficiency, reliability, and compact size. However, the control, fault diagnosis, and parameter estimation of PMSMs remain challenging tasks, particularly in dynamic and complex environments. The integration of artificial intelligence (AI) techniques has shown great promise in enhancing the performance, robustness, and accuracy of these processes. This paper provides a comprehensive review of the application of AI in the control, fault diagnosis, and parameter estimation of PMSMs. It explores various AI-driven methods, including machine learning, neural networks, fuzzy logic, and genetic algorithms, highlighting their effectiveness in improving system stability, fault tolerance, and adaptive control. The review also discusses challenges associated with implementing AI-based approaches, such as computational complexity and real-time processing requirements, and suggests potential solutions.