Machine Learning Controller for Synchronous Axis Control

The patent transitions from a static constant-speed prediction model to a dynamic prediction model that adapts to changing speeds. The learning section dynamically adjusts the prediction of master axis position based on actual operation states, allowing the system to maintain accuracy during acceleration, deceleration, and speed fluctuations while keeping the overall control structure manageable.

The patent implements feedback by using actual operation state data (position, speed, acceleration) to continuously refine and correct the predicted future position of the master axis. The learning section compares predicted positions with actual positions and adjusts prediction parameters accordingly, eliminating synchronization errors through continuous feedback loops.

The learning section performs self-learning by automatically adjusting prediction parameters using actual operation data from the master axis. The system serves itself by continuously improving its prediction accuracy through embedded learning algorithms without requiring external intervention or complex external learning systems, thus improving synchronization accuracy while limiting complexity growth.

The learning section serves multiple functions: it predicts future position, learns from actual operation data, corrects synchronization errors, and adapts to different operating conditions. This multi-functionality consolidates what could be separate complex systems into a single versatile component, improving accuracy without proportionally increasing overall system complexity.