Motor Control Apparatus with Machine Learning Parameter Adjustment
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Solution Overview
Problem
Conventional motor control apparatuses fail to adjust parameters effectively for feedback and feedforward controllers based on varying axis positions, leading to suboptimal performance due to fixed parameters and lack of adaptation to changing operating environments.
Innovation Solution
A motor control apparatus that utilizes machine learning to adjust parameters and switch controllers based on position command values and axis position information, employing a system with a switching determining part, machine learning part, and parameter holding part to optimize control parameters for each axis position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If parameters are fixed for feedback and feedforward controllers, then device complexity is reduced, but control precision deteriorates when operating conditions change
Solution Approach 1:
The patent implements dynamic parameter adjustment by introducing a machine learning part that continuously optimizes controller parameters based on actual operating conditions. The system transitions from static fixed parameters to dynamic adaptive parameters that automatically adjust according to axis position and environmental changes, resolving the contradiction between simplicity and precision.
Solution Approach 2:
The patent employs feedback mechanisms where the machine learning part receives information about actual control performance and operating conditions, then adjusts parameters accordingly. This closed-loop feedback system enables the controller to learn from past performance and continuously improve control precision without increasing overall system complexity.
2Measurement precision
If parameters are adjusted for each axis position using machine learning, then control precision is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service by enabling the controller to automatically adjust its own parameters through the machine learning part. The system performs self-optimization without requiring external intervention or complex manual configuration, allowing high precision control while maintaining relatively simple device architecture through autonomous adaptation.
3Adaptability or versatility
If controllers are switched based on axis position, then adaptability to changing conditions is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the control space into multiple axis position ranges, each associated with optimized parameters. The machine learning part automatically selects appropriate parameter sets based on the current axis position segment, enabling adaptive control without requiring complex multi-controller architectures. This segmented approach provides high adaptability while keeping the switching mechanism simple.
Data Source
AI summary
A motor control apparatus including a controller that controls a servo motor or a spindle motor and includes a switching determining part that determines a switching condition of the controller based on axis position information on a motor related to control of the motor control apparatus, a machine learning part that adjusts one or more parameters for the controller by machine learning for each switching condition, and a parameter holding part that holds the parameter adjusted by the machine learning part for each switching condition. The switching determining part, when determining the switching condition after adjustment of the parameter, uses the adjusted parameter corresponding to the switching condition in the controller. The apparatus enables changing, and automatic adjustment, of a parameter or controller to be used depending on a switching condition of the parameter related to axis position information or a switching condition of the controller using the parameter.