This research proposes a new system for controlling yarn tension in the winding operation based on active disturbance-rejection control (ADRC) technology, which controls and minimizes the fluctuation of tension during winding. The mechanical structure of the winding machine was analyzed to clarify the main factors affecting the yarn tension. Proportional–integral–derivative (PID) control is the most common control method of the winding machine. In order to compare the control effects of ADRC and PID control, the mathematical model of the yarn-tension control system was established on the MATLAB platform by means of system identification. Through the simulation on this model, the results showed that under the action of disturbance, the overshoot of the yarn tension controlled by the active disturbance-rejection controller was smaller, the adjustment time was shorter, and the steady-state error was smaller. An experimental platform was built to test the control effect of the controller under different parameters, thereby verifying the performance and stability of the active disturbance-rejection controller. The results showed that the controller had good performance and stability.
Diaz JuanMMartin-Ramos JuanAPrietoMAJ.Development of a deceleration control system for a winding machine based on a microcontroller. Ind Appl2002;
38: 1226–1231.
2.
LiuXMiaoX.Analysis of yarn tension based on yarn demand variation on a tricot knitting machine.Text Res J2017;
87: 487–497.
3.
PengZHMaG.Study on high accuracy constant tension controller of winding machine. Adv Mater Res2011; 328–330: 2292–5.
4.
Wei J, Zhang D and Chen R. Research on the control technique for optimal dispersion winding system. In: 2005 IEEE international conference mechatronics and automation (2005), Niagara Falls, ON, Canada, July 2005, pp. 1351–1356. IEEE. INSPEC Accession Number: 8946960.
5.
ZhaoHChangD.Research of factors affecting the yarn tension of a winding machine.Appl Mech Mater2014; 496–500: 1117–20.
6.
StanislavPNaceP.Fictitious forces in yarn during unwinding from packages.Text Res J2018; 88(2): 225–234.
7.
Xu QMei SQZhang ZM.Measurement method of yarn tension based on CCD technology. Adv Mater Res2011; 230–232: 89–93.
8.
Duong VTKim DHKim HK, et al.
Development of an active wire tension system for improving the performance of brushless direct current coil winding machine. Int J Adv Mechatron Syst2015; 6(5): 201–210.
9.
Wang ZNan HShi T, et al.
No-tension sensor closed-loop control method with adaptive PI parameters for two-motor winding system.Math Probl Eng2018;
2018: 24–37.
10.
Zhou QWei TQiu Y, et al.
Prediction and optimization of chemical fiber spinning tension based on grey system theory.Text Res J2019; 89(15): 3067–3079.
11.
Wang CCZhao HX.The control algorithm research of yarn tension for winding machine based on grey prediction model. Appl Mech Mater2014; 519–520: 1305–8.
12.
WangSHeFWangQ. Integral separation fuzzy PID control for winding tension system. In: the 31st Chinese control and decision conference (CCDC 2019), Nanchang, China, June 2019, pp. 4083–4088. IEEE. INSPEC Accession Number: 18975744.
13.
HeFHanJand WangQ.Fuzzy control with adjustable factors in tension control system.Adv Mater Res2014; 902: 201–6.
14.
XiongTZouDZhangQ, et al. Fuzzy fractional-order PID control of the winding web tension in flexible electronic roll-to-roll manufacturing process. In: the 33rd Chinese control and decision conference (CCDC 2021), Kunming, China, May 2021, pp. 5863–5868. IEEE. INSPEC Accession Number: 21480789.
15.
ZhouQGHuangDZ.Design of tension control system of fiberglass winding machine based on fuzzy logic control algorithm. Adv Mater Res2012; 429: 56–61.
16.
Yuan Y-X andYangJ. Rewinder’s tension control system based on fuzzy logic control algorithm.Chin Pulp Paper2006; 25: 29–31.
17.
HeFWangQ. Compensation and fuzzy control of tension in strip winding control system. In: proceedings of the 2012 7th IEEE conference on industrial electronics and applications (ICIEA 2012), Singapore, July 2012, pp. 22–27. IEEE. INSPEC Accession Number: 13149405.
18.
LuJ-SChengM-YSuK-H, et al.
Wire tension control of an automatic motor winding machine-an iterative learning sliding mode control approach.Robot Comput Integrat Manuf2018; 50: 50–62.
19.
AbjadiNRSoltaniJAskariJ, et al.
Nonlinear sliding-mode control of a multi-motor web-winding system without tension sensor.IET Contr Theor Appl2009;
3: 419–427.
20.
YanFFanKYanX, et al.
Constant tension control of hybrid active-passive heave compensator based on adaptive integral sliding mode method.IEEE Acc2020; 8: 103782–103791.
21.
BaoweiZJiuxiangSSunaZ, et al.
Prediction of yarn strength based on an expert weighted neural network optimized by particle swarm optimization.Text Res J2021; 91(23-24): 2911–2924.
22.
ZhengL.Tension control system design of a filament winding structure based on fuzzy neural network.Eng Rev2015; 35(1): 9–17.
23.
LiuGZhangS.PID control system for warp tension based on RBF neural network tuning.Chin J Text2008;
29: 96–99 +107.
24.
WeiX.Research on coiler tension control system based on BP neural network. Thesis, Northeastern University, 2013.
25.
ZhuYZhouXWuT, et al.
Design of filament constant tension control system based on fuzzy neural network. Appl Single Chip Microcomput Embed Syst2019;
19: 72–75+80.
26.
WangCWangYYangR, et al.
Research on precision tension control system based on neural network. IEEE Trans Ind Electron2004;
51: 381–386.
27.
OwensDHHatonenJ.Iterative learning control – an optimization paradigm. Ann Rev Contr2005;
29: 57–70.
