Motor Control System Using Reinforcement Learning for Parameter Tuning
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Solution Overview
Problem
Conventional motor control systems require manual adjustment of operation parameters, which is time-consuming and labor-intensive, necessitating a more efficient method to achieve desired output characteristics.
Innovation Solution
A machine learning system that includes a state observer, motor output calculator, reward calculator, and learning unit to automatically adjust motor parameters by observing rotation number, torque, current, and voltage values, using reinforcement learning to update an action value table and determine optimal parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustment of operation parameters is used, then desired output characteristics can be obtained, but many man-hours are required
Solution Approach 1:
The motor control system performs self-learning and self-adjustment of operation parameters through the learning unit that automatically optimizes parameters based on observed motor states and calculated rewards, eliminating the need for manual parameter tuning while achieving desired output characteristics
Solution Approach 2:
The patent replaces manual mechanical adjustment with an automated machine learning system that uses state observation, reward calculation, and action value table updates to automatically determine optimal operation parameters, substituting human operators with an intelligent control algorithm
2Manufacturing precision
If manual adjustment of operation parameters is used, then desired output characteristics can be obtained, but the process is labor-intensive
Solution Approach 1:
The learning unit automatically performs parameter optimization by observing motor states through the state observer and updating the action value table based on calculated rewards, enabling the system to self-adjust parameters without requiring operator intervention or expertise
Solution Approach 2:
The system implements a closed-loop feedback mechanism where the state observer continuously monitors motor parameters, the reward calculator evaluates performance based on desired output characteristics, and the learning unit adjusts operation parameters accordingly, creating an automated adaptive control system
Data Source
AI summary
A machine learning system according to an embodiment of the present invention includes a state observer for observing the rotation number, torque, current, and voltage values of a motor detected by a motor controller for driving the motor; a motor output calculator for calculating a motor output from the rotation number, torque, current, and voltage values of the motor observed by the state observer; a reward calculator for calculating a reward based on the motor output; and a learning unit for updating an action value table based on the rotation number, torque, current, and voltage values of the motor.