Servo Filter Coefficient Tuning for Adaptive Vibration Attenuation
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Solution Overview
Problem
In servo control devices, optimizing filter characteristics is challenging due to inter-axis position and velocity gain dependencies, leading to potential oscillations even when machine characteristics are not affected by other axes, necessitating adaptive filter adjustments.
Innovation Solution
A machine learning device employing reinforcement learning to optimize filter coefficients by acquiring state information on input/output gains and phase delays, adjusting coefficients based on evaluation values, and updating action value functions to minimize vibrations across varying conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If filter coefficients are optimized for a specific axis position or velocity gain, then vibration attenuation is improved at that condition, but oscillation occurs when the axis position or velocity gain changes
Solution Approach 1:
The filter coefficients are made dynamically adjustable based on the detected oscillation characteristics. The system continuously monitors the servo control device's response and adapts the filter parameters in real-time to match changing operating conditions, transforming a static filter into a dynamic one that maintains effectiveness across varying positions and velocity gains
Solution Approach 2:
The system implements feedback by detecting oscillation characteristics (frequency and amplitude) from the servo control device output and using this information to adjust the filter coefficients. This closed-loop approach ensures the filter adapts to changing conditions by continuously responding to actual system behavior rather than relying on pre-set parameters
2Reliability
If multiple filters are added to handle different axes and conditions, then vibration attenuation across all conditions improves, but device complexity increases
Solution Approach 1:
A single filter structure is designed to perform multiple functions by dynamically adjusting its coefficients. Rather than implementing separate filters for each axis position or velocity gain condition, the system uses one adaptable filter that can be tuned to handle various oscillation characteristics, reducing the number of components needed while maintaining comprehensive vibration attenuation
Solution Approach 2:
The system changes the parameters (coefficients) of the existing filter based on detected oscillation characteristics rather than adding more filters. By modifying the filter's mathematical parameters in response to different operating conditions, the system achieves adaptive vibration attenuation without increasing structural complexity or the number of filter elements
Data Source
AI summary
A machine learning device that performs reinforcement learning for a servo control device and optimizes a coefficient of a filter for attenuating a specific frequency component provided in the servo control device includes a state information acquisition unit which acquires state information that includes the result of calculation of at least one of an input/output gain of the servo control device and a phase delay of input and output, the coefficient of the filter and conditions, and an action information output unit which outputs, to the filter, action information including adjustment information of the coefficient. A reward output unit determines evaluation values under the conditions based on the result of the calculation to output, as a reward, the value of a sum of the evaluation values. A value function updating unit updates an action value function based on the value of the reward, the state information and the action information.