Abstract
This paper investigates the warp let-off and take-up mechanism of rapier looms to solve the problem that the warp tension of rapier looms fluctuates greatly and the warp let-off is difficult to maintain constant. The design and hardware implementation of a let-off and take-up control system based on fuzzy neural network (FNN) and vector control (VC) are presented to improve the control level of warp tension and drive performance of the let-off and take-up system. Firstly, the spring-damper dynamic model of the warp is established according to the mechanical properties. The parametric expression of warp tension and the control strategy of fixed angle interval based on let-off and take-up motions are constructed according to the generation mechanism and fluctuation law of warp tension. Then, based on fuzzy reasoning mechanism and neural network model, the fusion theory of fuzzy neural network is introduced, and a tension controller based on T-S fuzzy neural network (FNN) is designed. FNN is trained by introducing genetic optimization and the backpropagation fusion algorithm (GA-BP). In addition, a specialized let-off and take-up hardware circuit is constructed through embedded technology, and the SVPWM algorithm is used as the driving scheme of the hardware circuit. Finally, simulation and actual weaving experiments test the proposed let-off and take-up control system and hardware circuit. The results show that, compared to PID and fuzzy PID, the proposed fuzzy neural network algorithm has higher tension control accuracy and can effectively restrain the rapier loom’s warp tension undulation. The designed hardware circuit and SVPWM algorithm have a fast and stable driving ability, which ensures the constant let-off amount.
Highlights
The rapier loom is a kind of shuttleless loom widely used in the textile industry because of its high speed, high degree of automation, and good production efficiency [1]
This paper introduces the design of the control system of the let-off and curling system of a rapier loom and the realization of the hardware circuit
Aiming at the time-varying, nonlinear and multi-coupling characteristics of warp tension, on the basis of PID control, by introducing fuzzy inference and neural network fusion theory, a tension closed-loop control system based on T-S fuzzy neural network is designed
Summary
The rapier loom is a kind of shuttleless loom widely used in the textile industry because of its high speed, high degree of automation, and good production efficiency [1]. Wang [7] investigated the structure and control method of the loom's let-off and take-up mechanisms and developed a let-off and take-up control model based on a fuzzy neural network adaptive PID algorithm. Based on the fuzzy neural network (FNN) and vector control method, a new let-off and take-up controller for rapier loom is proposed in this paper. A fuzzy neural network tension control strategy is proposed based on genetic and back propagation algorithms (GA-BP FNN) This method is appropriate for controlling loom warp tension with time-varying, nonlinear and multivariable coupling. The application of the GA-BP algorithm greatly improves the training speed and accuracy of FNN It provides a practical and effective method to improve rapier loom's tension control accuracy and weaving quality. The motor no-load experiment and weaving experiment show that it has good drive stability in the rapier loom
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