Servo Stiffness Tuning Using Vibration Covariance Signals
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Solution Overview
Problem
Existing servo system stiffness tuning strategies rely on special loci, limiting their applicability and requiring tedious manual debugging.
Innovation Solution
A method and device for tuning servo system stiffness by reconstructing characteristic signals, calculating vibration characteristic covariance coefficients, and adjusting stiffness based on the system's own parameters without relying on special loci, enhancing compatibility and intelligence of the algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If time-domain criteria with preset special loci are used for parameter auto-tuning, then the tuning process can be automated, but the applicability of the algorithm is decreased
Solution Approach 1:
The patent transforms the tuning algorithm from a specialized tool requiring preset loci into a universal solution that works with any motion trajectory. By using covariance analysis of vibration characteristics that appears in any periodic motion, the algorithm achieves multi-functionality across different application scenarios without requiring trajectory-specific configurations.
Solution Approach 2:
The patent changes the fundamental parameter used for tuning judgment from trajectory-dependent features to trajectory-independent vibration covariance characteristics. This parameter transformation allows the algorithm to maintain automation while becoming applicable to diverse motion patterns, as the vibration characteristics remain consistent regardless of the specific trajectory used.
2Ease of manufacture
If special loci are used for stiffness tuning, then the tuning strategy can be implemented, but the compatibility and intelligence degree of the algorithm is reduced
Solution Approach 1:
The patent enables the servo system to perform self-diagnosis and self-tuning by automatically analyzing its own vibration characteristics during normal operation. The system extracts covariance coefficients from its own motion data and uses these to determine optimal stiffness parameters, eliminating the need for external specialized testing procedures or manual intervention.
Solution Approach 2:
The patent implements a feedback mechanism where the servo system continuously monitors its vibration characteristics, calculates covariance coefficients, and adjusts stiffness parameters based on this feedback. This closed-loop approach increases algorithm intelligence by enabling real-time, data-driven parameter optimization without requiring preset loci or manual tuning.
Data Source
AI summary
Disclosed is a method for tuning stiffness of a servo system, including: reconstructing a characteristic signal of a servo system; calculating a vibration characteristic covariance coefficient of the servo system according to the characteristic signal; and tuning the stiffness of the servo system according to the calculated vibration characteristic covariance coefficient. By reconstructing the characteristic signal of the servo system, relationships between parameters of the system and the characteristic signal of the system are determined, and then, system stiffness tuning is carried out by calculating the vibration characteristic covariance coefficient of the system. In the process of tuning the stiffness of the servo system, a tuning judgment is made according to the system's own parameters without relying on a special locus, thus increasing the compatibility and intelligence degree of an algorithm.