Machine Learning Motor Current Correction

The patent transforms the static correction value system into a dynamic one by introducing a machine learning apparatus that continuously learns and updates correction values based on real-time operational data. The system dynamically adjusts current feedback offset correction, inter-phase unbalance correction, and dead zone correction values according to varying temperature and voltage conditions, thereby maintaining high rotor position control accuracy without compromising system stability through progressive adaptation.

The machine learning apparatus enables the control system to self-optimize by automatically learning optimal correction values from operational data without requiring external manual calibration. The system performs self-diagnosis and self-adjustment of correction parameters based on observed errors between commanded and actual rotor positions, eliminating the need for periodic manual recalibration while maintaining both stability and accuracy.

The machine learning apparatus performs preliminary learning during idle or low-load periods when computational resources are available, building up correction value models in advance. This pre-learning approach allows the system to have correction strategies ready before critical control decisions are needed, reducing real-time computational burden while maintaining high accuracy through previously learned patterns.

The system implements continuous feedback loops where the actual rotor position (from observers) is compared with commanded positions, and the error signals are fed back to the machine learning apparatus for ongoing refinement of correction values. This feedback mechanism enables incremental learning that progressively improves accuracy without requiring complex recomputation, as the system builds upon previously learned patterns with each new data point.