SPEED CONTROL OF PERMANENT MAGNET SYNCHRONOUS MOTOR USING AN ARTIFICIAL INTELLIGENCE CONTROLLER

Abstract

Permanent Magnet Synchronous Motors (PMSMs) are widely adopted in high-performance industrial
drives, electric vehicles, robotics, and renewable energy systems due to their high efficiency, compact
size, and superior torque density. However, achieving precise and robust speed control of PMSMs
remains a challenging task because of nonlinear dynamics, parameter variations, load disturbances, and
uncertainties in operating conditions. Conventional proportional–integral (PI) controllers, although simple
and widely used, exhibit limited performance under such nonlinear and time-varying conditions. In recent
years, Artificial Intelligence (AI)–based control techniques have emerged as powerful alternatives capable
of learning system behavior, adapting to uncertainties, and improving dynamic performance. This paper
presents a comprehensive study on speed control of a PMSM using an AI controller, with emphasis on
intelligent techniques such as Artificial Neural Networks (ANN), Fuzzy Logic Control (FLC), and hybrid
neuro-fuzzy approaches. The mathematical modeling of PMSM in the d–q reference frame is discussed,
followed by the design methodology of the AI-based speed controller integrated with field-oriented
control. Performance evaluation is carried out in terms of rise time, settling time, steady-state error,
torque ripple, and robustness against load disturbances. Comparative analysis with conventional PI
control demonstrates the superiority of AI controllers in achieving faster dynamic response, reduced
overshoot, and improved stability. The results highlight the suitability of AI-based speed control
strategies for next-generation high-performance PMSM drive applications.