Improved Model-Free Predictive Control for Permanent Magnet Synchronous Motors

The conventional model predictive current control (MPCC) technique has been widely recognized as an effective solution for three-phase inverter applications, demonstrating notable characteristics including conceptual simplicity. Nevertheless, this control methodology remains highly susceptible to variations in machine parameters, with substantial deterioration in control performance observed when discrepancies occur between modeled electrical characteristics and actual motor parameters. To address these limitations, this paper proposes a Model-Free Predictive Current Control with Extended State Observer (MFPCC-ESO) method, integrating the ultralocal model and Extended State Observer (ESO). that eliminates the dependency on motor parameters during the controller development phase. Through innovative integration of an enhanced extended state observer (ESO) architecture with the ultra-local system modeling approach, a model-free predictive current regulation framework is established. The developed model-free predictive current control (MFPCC) system demonstrates significant practical advantages by substantially minimizing parameter tuning requirements and computational complexity, while simultaneously achieving enhanced tracking accuracy and dynamic responsiveness. Comparative evaluation with conventional MPCC implementations and existing modelfree solutions confirms the technical superiority of the proposed methodology, with its operational effectiveness rigorously verified via comprehensive simulation studies and experimental verification.