Abstract
Tension control is very important for the intelligent control system of rapier looms because stable tension guarantees a tight fabric structure, moderate elasticity and good forming. To solve the problems of low-tension measurement accuracy of the existing rapier loom, the complex structure of the tension control strategy and algorithm, and the high research and development cost, this paper proposes new research from the perspective of high-precision and nonlinear processing of tension detection signals. The median filtering and limiting filtering algorithms are integrated to solve the uncertainty problem caused by the disturbance and fluctuation of the tension signal, and an excellent sample dataset is obtained. The attenuation factor and the number of learning times are introduced to design and adjust the learning rate of the back propagation neural network algorithm. In addition, the overfitting problem of the backpropagation neural network model in the current research process is solved. The experimental simulation results show that the tension detection fluctuation range of this method is 0.8 kg, and the tension detection error is within 0.1%. On the basis of the existing tension control algorithm, the tension detection accuracy is improved, which presents a new research perspective for the wide application of high-precision tension control strategies in rapier looms. It is of great importance to improve the production efficiency and fabric quality of rapier looms.
Highlights
Rapier looms have many advantages, such as high speed, high automation, high-efficiency production and strong adaptability of manufacturing varieties
To solve the problems of low-tension measurement accuracy of the existing rapier loom, the complex structure of the tension control strategy and algorithm, and the high research and development cost, this paper proposes new research from the perspective of high-precision and nonlinear processing of tension detection signals
This paper proposes a new tension control strategy for rapier looms based on the fusion of a back propagation (BP) neural network and a digital filter algorithm
Summary
Rapier looms have many advantages, such as high speed, high automation, high-efficiency production and strong adaptability of manufacturing varieties. Based on the nonlinear model of rapier loom tension detection and control, as well as on the principle of neural network parameter setting, a novel learning rate calculation model is proposed. 2) A new research perspective is proposed considering the high precision and nonlinearity of the tension detection signal This furthers the existing research that only considered the control tension level, which caused the processed signal to have a relatively large error, and required an improvement of the tension control accuracy. The tension control strategy has a simple algorithm structure and a small amount of calculation It greatly reduces the research and development costs of the system and provides a theoretical basis for the popular application of high-precision rapier loom tension control strategies in universal loom equipment
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