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<p>This paper has developed the advanced QIMNSO for enhancement in the DTC of induction motors through a set of proposed schemes on quantum computing, memetic algorithms, adaptiveness of a neural network, and swarm intelligence. The proposed QIMNSO-DTC is designed to avoid main drawbacks inherent in traditional DTC, which are named as torque ripple, response lag, and energy inefficiency-evidently committed under dynamic load conditions. Application of QIMNSO results in prompt torque and flux adjustment, smaller ripples, and reduction of mechanical stress to the motor. This neural network part allows the real-time adaptation of parameters to achieve the best performance for all operating conditions and load variations. Simulation results indicate that QIMNSO-DTC enjoys some merits in comparison to classical control methods, including FOC, SMC, and PID controllers in terms of torque stability, response speed, energy efficiency, and self-adaptiveness. Such enhancements render QIMNSO-DTC very fit for applications where induction motors should be precisely, efficiently, and reliably controlled, such as robotics, electric vehicles, and high-performance industrial drives. QIMNSO represents a promising, scalable control approach in the present research that gives classical methods a large margin of improvement and contributes to further innovation related to intelligent motor control.</p>

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Advanced direct torque control of induction motors with quantum-inspired memetic neural swarm optimization (QIMNSO) for improved torque stability and energy efficiency

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月13日

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  • Mustafa E. I. Mohammed,
  • A. H. Gomaa Haroun

摘要

This paper has developed the advanced QIMNSO for enhancement in the DTC of induction motors through a set of proposed schemes on quantum computing, memetic algorithms, adaptiveness of a neural network, and swarm intelligence. The proposed QIMNSO-DTC is designed to avoid main drawbacks inherent in traditional DTC, which are named as torque ripple, response lag, and energy inefficiency-evidently committed under dynamic load conditions. Application of QIMNSO results in prompt torque and flux adjustment, smaller ripples, and reduction of mechanical stress to the motor. This neural network part allows the real-time adaptation of parameters to achieve the best performance for all operating conditions and load variations. Simulation results indicate that QIMNSO-DTC enjoys some merits in comparison to classical control methods, including FOC, SMC, and PID controllers in terms of torque stability, response speed, energy efficiency, and self-adaptiveness. Such enhancements render QIMNSO-DTC very fit for applications where induction motors should be precisely, efficiently, and reliably controlled, such as robotics, electric vehicles, and high-performance industrial drives. QIMNSO represents a promising, scalable control approach in the present research that gives classical methods a large margin of improvement and contributes to further innovation related to intelligent motor control.