Multi-Axis Servo Compensation Using Reinforcement Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing servo control systems face challenges in compensating for interference between axes, which affects the command followability of motors driving machines with multiple axes, leading to reduced precision and efficiency in operations like those in machine tools and robots.
Innovation Solution
A machine learning device is integrated into the servo control system to learn and adjust compensation values for position errors, velocity commands, and torque commands based on position feedback information from interfering axes, using reinforcement learning to optimize coefficients and minimize interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is used to automatically adjust compensation values, then command followability and precision are improved, but device complexity increases
Solution Approach 1:
The servo control unit automatically adjusts compensation values through machine learning without requiring manual intervention. The system performs self-learning by acquiring state information, calculating reward values based on evaluation functions, and updating value functions autonomously, thereby improving command followability while avoiding the complexity of manual adjustment mechanisms
Solution Approach 2:
Traditional manual adjustment mechanisms are replaced with a machine learning-based computational system. The patent substitutes mechanical adjustment procedures with automated algorithms that process state information, compute reward values, and update compensation parameters through value function optimization, reducing physical complexity while enhancing precision
2Device complexity
If manual adjustment of compensation values is performed, then device complexity is reduced, but manufacturing precision and command followability deteriorate
Solution Approach 1:
The system implements automated feedback loops where the servo control unit continuously acquires state information from multiple axes, calculates reward values based on positioning errors and interference levels, and adjusts compensation values accordingly. This feedback mechanism enables precise positioning by dynamically optimizing compensation parameters based on real-time system state
Solution Approach 2:
The patent dynamically changes compensation parameters through machine learning optimization. The value function is updated based on reward values that reflect positioning accuracy and interference mitigation effectiveness, allowing the system to adapt compensation parameters optimally for different operating conditions and achieve high manufacturing precision
3Productivity
If reinforcement learning is implemented to optimize compensation coefficients, then productivity and precision are improved, but loss of time during learning increases
Solution Approach 1:
The system performs learning operations during periods when the servo system is not actively processing production tasks. By acquiring state information and updating value functions during idle or low-utilization periods, the system prepares optimized compensation parameters in advance, minimizing the impact on production productivity while achieving high precision
Solution Approach 2:
The machine learning process operates continuously or periodically in the background without interrupting normal servo control operations. The system maintains continuous optimization of value functions using ongoing state information, ensuring that productivity is maintained while precision improvements are accumulated over time through continuous learning
Data Source
AI summary
A machine learning device performs machine learning with respect to a plurality of servo control units corresponding to a plurality of axes. A first servo control unit related to an axis receiving interference includes a compensation unit that compensates for at least one of a position error, a velocity command, and a torque command of the first servo control unit based on at least one of a variable related to a position command and a variable related to position feedback information of a second servo control unit related to an axis generating the interference. The machine learning device acquires state information including first and second servo control information and a coefficient of the function, outputs action information and a reward value for reinforcement learning, and updates a value function on the basis of the reward value, the state information, and the action information.