Robust Deadbeat Predict Current Control Using Intelligent Integral Sliding Mode Control for Interior Permanent Magnet Synchronous Motor Drive

This study presents a robust deadbeat predictive current control (DPCC) strategy for an interior permanent magnet synchronous motor (IPMSM) drive. The proposed scheme integrates an adaptive neural network (ANN) with an intelligent integral sliding mode control (ISMC). Since DPCC is sensitive to variations in motor drive parameters and external disturbances, enhancing its robustness is crucial. To address these challenges, the study focuses on mitigating parameter sensitivity and improving the disturbance rejection capability of DPCC. First, the modeling and control strategies of DPCC are derived, incorporating the effects of time delays on the dq-axis of the IPMSM. Disturbance terms for the dq-axis are also formulated. Next, a detailed analysis of ISMC is provided, demonstrating its ability to manage model parameter mismatches and disturbances in dq-axis current control for the IPMSM drive. To further optimize performance, an ANN is employed to estimate the dq-axis disturbance terms, enabling a reduction in the switching gains of the ISMC and resulting in a more efficient intelligent ISMC. Finally, experimental results are presented to validate the effectiveness of the proposed robust DPCC using intelligent ISMC for the IPMSM drive, particularly in the constant torque region.