Speed and Torque Optimization of Motor Drive Through Intelligent Control Approaches

The need for better control approaches in Permanent Magnet Synchronous Motors (PMSMs) has increased due to the increased usage of electric motors in many different areas. The unique characteristics of PMSM motors make it difficult to use conventional control techniques to dynamically adjust speed and torque. This research proposes a novel approach to real-time speed and torque optimization of PMSM motor drives by integrating Sliding Mode Control (SMC) in Reinforcement Learning (RL). This could alleviate these issues. This study introduces a dual-mode control approach for permanent magnet synchronous motors (PMSM) that leverages the robustness of SMC and the flexibility of RL. In order to improve the PMSM motor drive’s overall efficiency, a synergistic effect is achieved by creating an RL-based Maximum Torque and Speed Tracking (MTST) algorithm. Incorporating SMC ensures precise and stable control of speed and torque. Results from both theoretical and practical investigations using a PMSM motor operating system model in Matlab/Simulink validate the proposed method. The results reveal improved control over speed and torque, proving the efficacy and adaptability of the proposed method for PMSM actuators to function in different environments.