Hybrid AI-Based Torque and Flux Optimization Technique for Next-Generation Electric Motor Drives

Abstract

Electric motor drives are essential to the modern technological system including industrial automation, electric vehicles, robotics, and renewable energy conversion systems. One of the significant issues with high performance motor drive is achieving efficiency in torque and flux regulation and reduce power losses and stability of the system. Traditional control schemes, such as Field-Oriented Control (FOC) and Direct Torque Control (DTC), have become very popular because of their ability to control the performance of the motor. But these approaches do have a number of shortcomings: they can be sensitive to parameter changes, have a torque ripple, and are difficult to tune, as well as not being as adaptable to nonlinear and dynamic operating environments. To mitigate these issues, the given study offers a Hybrid Artificial Intelligence (AI)-assisted torque and flux optimization method to the electric motor drives of the next generation. The suggested structure incorporates the latest machine-learning algorithms with conventional control systems in order to improve motor drive effectiveness and efficiency. More precisely, Deep Neural Networks (DNN) predictive estimation of the optimal torque and flux reference values are coupled with Reinforcement Learning (RL) to make continuous control policy adjustments to the real-time operating conditions. The hybrid AI architecture allows the making of intelligent decisions and optimization that results in adaptability when it comes to the motor control parameters. The designed method is tested and assessed via simulation by means of Permanent Magnet Synchronous Motor (PMSM) drive model. The simulation outcomes indicate that the given method could greatly decrease the torque ripple, increase the overall energy efficiency, and dynamic reactivity in contrast to the traditional control techniques. Also, the system is highly robust to disturbances in load and changes in parameters. Thus, the paper suggested AI-based optimization strategy in hybrid form, which can be a promising solution to better the performance of new generation electric motor drive systems of electric vehicles, smart manufacturing and industrial automation purposes.