SVM-DTC Based Direct Torque Control for Permanent Magnet Synchronous Motors

Direct Torque Control based on Support Vector Machine-DTC (SVM-DTC) for Permanent Magnet Synchronous Motors represents an advanced motor control technology. This approach utilizes Support Vector Machine algorithms to enhance the performance and efficiency of permanent magnet synchronous motors. Support Vector Machines are machine learning algorithms capable of performing classification and regression analysis based on input data. In the context of permanent magnet synchronous motor control, SVM is employed to optimize motor parameters, enabling precise torque control under varying load conditions. From an implementation perspective, the SVM-DTC system typically involves MATLAB/Simulink modeling where the SVM module processes real-time motor data (such as stator currents and rotor position) to generate optimal switching signals for the inverter. The algorithm implementation would include kernel function selection (e.g., RBF kernel) for nonlinear parameter mapping and hyperparameter tuning through cross-validation techniques. This control methodology significantly improves motor response speed and torque accuracy, thereby enhancing overall system performance and efficiency. The code implementation generally features torque and flux hysteresis controllers working in conjunction with SVM-based modulation to reduce torque ripple while maintaining rapid dynamic response. Consequently, SVM-DTC based direct torque control for permanent magnet synchronous motors represents a valuable advanced technology worthy of research and practical application in modern drive systems.