Electric Motor Controller Learning Data Correction

The learning control is segmented into multiple learning sections (first learning section for periodic disturbances at a first learning period, second learning section for local changes at a second learning period). Each section handles specific types of deviations independently, allowing the system to maintain convergence reliability across different operational conditions without requiring a single complex learning mechanism.

The learning control system dynamically switches between different learning periods based on the type of deviation being corrected. The controller adapts the learning period according to the operational phase - using the first learning period for regular periodic disturbances and the second learning period for local changes during cutting operations, thereby maintaining reliability across dynamic conditions.

The learning control unit is divided into multiple learning sections, each dedicated to analyzing deviations within a specific learning period. The first learning section handles periodic disturbances with the first learning period, while the second learning section handles local changes with the second learning period. This segmentation enables precise differentiation and correction of different deviation types without requiring a single complex analysis mechanism.

Instead of attempting to correct all positional deviations with a single learning period, the system applies partial learning actions tailored to specific deviation types. The first learning section corrects periodic disturbances, while the second learning section corrects local changes. This partial action approach achieves measurement precision by focusing each learning section on its specific deviation type rather than trying to handle all deviations uniformly.

The learning control cycle is segmented into parallel processing paths with different learning periods. The first learning section operates with the first learning period to correct periodic disturbances, while the second learning section operates with the second learning period to correct local changes. This segmentation enables accurate correction of multiple disturbance types simultaneously, achieving manufacturing precision without requiring a single extended learning cycle.

The multiple learning sections operate continuously and independently, with each section continuously correcting its specific type of deviation. The first learning section continuously corrects periodic disturbances while the second learning section continuously corrects local changes. This continuous parallel action ensures manufacturing precision is maintained without interruption, avoiding the time loss that would occur if a single learning cycle had to complete all corrections sequentially.

The learning-data correcting section extracts and removes the influence of local changes from the positional deviation data before storing it as learning data. By separating the local change component (handled by the second learning section) from the periodic disturbance component (handled by the first learning section), the system stores cleaner, more accurate learning data that improves control reliability without requiring complex filtering mechanisms.

The learning-data correcting section performs preliminary correction of positional deviation data by removing local change influences before the data is stored and used for future control. This preliminary action ensures that the learning data stored in memory is already corrected and ready for accurate control applications, improving reliability without requiring complex real-time correction during the control cycle.