An intelligent speed controller for indirect vector-controlled induction motor with high efficiency taking core loss into account

Abstract

To address high-performance drive operations, this work details a genetic algorithm (GA)-tuned proportional integral (PI) control strategy applied to sensorless indirect vector-controlled induction motors (IM), explicitly embedding core loss dynamics within the loop. Although GA-based PI tuning methods have been extensively studied, most existing approaches neglect iron loss dynamics, leading to reduced modeling accuracy and suboptimal energy efficiency. The proposed method simultaneously optimizes PI speed controller gains using GA while integrating core loss resistance into the motor model. This combined optimization enhances both dynamic performance and energy efficiency under varying load and speed conditions. Simulation results demonstrate that the proposed PI-GA controller reduces settling time by 62% compared to a classical PI controller, while overshoot decreases from 18% to 5%. Total harmonic distortion (THD) is limited to 3.4%, and iron losses are reduced by approximately 15%, resulting in an overall efficiency improvement up to 95.2%. Comparative analysis confirms the robustness and superiority of the proposed strategy, highlighting its suitability for high-performance and energy-efficient IM drive applications.