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Abstract

Permanent Magnet Synchronous Motors (PMSMs) have been widely employed in numerous applications, on account of their high efficiency, compact structure, and high-performance torque control. Nevertheless, the dynamic and uncertain control PMSMs under the action of system perturbation and variation is a difficult problem in the field of traditional control law by using Proportional Integral Derivative controller (PID),Field oriented control (FOC) and Direct Torque Control (DTC) etc. In this article, we offer a critical survey of intelligent control methods such as Fuzzy Logic Control (FLC), Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), Evolutionary Algorithms (EAs) and discuss their advantages in accommodating nonlinearity, uncertainty of the system and real-time adaptability. By these methods, the performance and energy should be comparable/more optimal, and robust to the traditional setting. Moreover, the paper also discusses practical issues such as computation complexity, hardware complexity, and power consumption, advocating hybrid or simplified control models as possible solutions. Finally, the outlook for smart PMSM control is also presented, especially in EVs and renewable areas.

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