Spindle blade motion state control method and system for wrap yarn spinning
By constructing the coated yarn textile state vector and adaptive controller, the problem of insufficient accuracy of the traditional spindle motion control method in complex environments is solved, efficient yarn coating control is achieved, and yarn quality and production efficiency are improved.
CN119753905AActive Publication Date: 2025-04-04SHENGZHOU NANFENG MASCH CO LTD
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Patent Information
- Application Number
- CN202510268533.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-07
- Publication Date
- 2025-04-04
- Estimated Expiration
- 2045-03-07
AI Technical Summary
Technical Problem
Traditional spindle motion control methods lack adaptive and optimization capabilities, making it difficult to achieve high-precision yarn coating control in complex and changeable production environments, affecting the quality and production efficiency of yarns.
Method used
By constructing the coated yarn textile state vector, calculating the importance weight, using a tension prediction model and an adaptive controller, combined with an online parameter optimization method, precise control of the spindle rod is achieved.
Benefits of technology
It improves the uniformity and consistency of yarn coating, enhances the adaptability of the production environment, and significantly improves control accuracy and production efficiency.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention relates to the technical field of motion state control, in particular to a spindle blade motion state control method and system for wrap yarn spinning. Yarn core tension, motor operation data and environment data are collected to construct a wrap yarn spinning state vector, the importance weight of each element in the wrap yarn spinning state vector to tension change is calculated, and the wrap yarn spinning state vector is weighted according to the importance weight to obtain a weighted state vector; on the basis of the weighted state vector, the predicted yarn core tension at the next sampling moment is obtained through a tension prediction model; calculating a tension error, predicting the tension error and a tension error change rate, inputting the tension error and the tension error change rate into a self-adaptive controller to obtain a motor current control quantity, calculating a motor voltage control quantity according to the motor current control quantity, controlling a spindle blade according to the motor voltage control quantity, and adjusting parameters of the self-adaptive controller in real time through an online parameter optimization method. According to the invention, the spindle blade control precision of wrap yarn spinning can be improved.
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