Servo Control Parameter Updating With Vibration Threshold Feedback
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Solution Overview
Problem
Conventional control parameter adjustment in production devices is time-consuming and requires skilled technicians, and adjustments may be adversely affected by environmental disturbances, leading to recurring issues with vibration-induced noise and vibration exceeding thresholds.
Innovation Solution
A control parameter generation method using a sensor to measure the position of an object driven by a servomotor, generating evaluation index data on vibration, and updating the control parameter with a machine learning model only when vibration is within a threshold, with interruptions for excessive vibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If control parameter adjustment is performed manually by skilled technicians, then the control parameter can be adjusted with expertise, but the process is time-consuming and requires human intervention
Solution Approach 1:
The system performs self-adjustment of control parameters by automatically acquiring vibration data, evaluating whether vibration exceeds thresholds, and updating parameters without human intervention. The control device autonomously completes the entire adjustment process that previously required skilled technicians, eliminating both time loss and dependency on human expertise.
Solution Approach 2:
The patent replaces manual mechanical adjustment with an automated control system that uses sensors to detect vibration and a control device to update parameters. This substitution of manual operation with an automated system resolves the contradiction by providing both speed and precision simultaneously.
2Measurement precision
If control parameter adjustment is performed manually, then skilled technicians can make precise adjustments, but environmental disturbances adversely affect the adjustments causing recurring vibration issues
Solution Approach 1:
The system continuously monitors vibration through sensors and uses this feedback to determine whether control parameter updates are needed. By establishing a feedback loop where vibration data directly informs adjustment decisions, the system achieves both precision and reliability, eliminating the adverse effects of environmental disturbances that plagued manual adjustments.
Solution Approach 2:
The control device evaluates vibration data before proceeding with parameter updates, ensuring that adjustments are only made when appropriate. This preliminary evaluation prevents unnecessary adjustments that could be adversely affected by environmental disturbances, thereby improving reliability while maintaining precision.
3Productivity
If control parameters are updated continuously, then the system adapts quickly to vibration issues, but unnecessary updates increase when vibration is already within acceptable thresholds
Solution Approach 1:
The system performs parameter updates only partially - specifically, only when vibration evaluation indicates it is necessary. By applying the action of parameter update selectively rather than continuously, the system achieves fast adaptation when needed while avoiding unnecessary updates that would waste energy and resources.
4Ease of manufacture
If manual control parameter adjustment is used, then the process is simple to implement, but it requires skilled technicians and is time-consuming
Solution Approach 1:
The control device autonomously performs the entire parameter adjustment process without requiring skilled technicians. The system self-manages data acquisition, vibration evaluation, and parameter updates, thereby maintaining implementation simplicity while dramatically improving adjustment efficiency and productivity.
Data Source
AI summary
A control parameter generation method used in a production device, the method comprising: acquiring measurement data indicating a position from a sensor that measures the position of an object to be driven; and generating, based on the measurement data, evaluation index data indicating vibration of the object to be driven after a time at which the object to be driven has arrived. In a case where the vibration indicated by the evaluation index data is equal to or less than a predetermined threshold, based on the evaluation index data, the control parameter is updated using a machine learning model that learns the relationship between the evaluation index data and the control parameter, and in a case where the vibration indicated by the evaluation index data exceeds the predetermined threshold, the update of the control parameter is interrupted.


