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The Equivalent Sliding Mode Tension Control of Carbon Fiber Multilayer Diagonal Loom

  • Guowei XuEmail author
  • Ruxia Zhou
  • Wei Liu
  • Fangrui Hao
Article
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Abstract

In order to enhance the tension control performance of the carbon fiber multilayer diagonal loom, two control methods based on the yarn warping and the curling control are proposed. The tension control mathematical models of carbon fiber multilayer diagonal loom, the yarn warping control model and the curling control model, are reestablished by the force analysis of the warping and the curling systems. Some technological process movements, such as the opening and the beating-up, are regarded as the disturbances. Based on the equivalent sliding mode control theory, the tension control strategies of the warping and the curling are constructed. Lyapunov method is used to prove the stability of the systems. Simulation results show that both the two methods could control the tension of the yarn, and the control results have good robustness to the disturbances.

Keywords

Carbon fiber multilayer diagonal loom curling slide mode control warping yarn tension control 

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References

  1. [1]
    W. Liu, G.W. Xu, and X. M. Jiang, “Discrete global sliding mode control for time-delay carbon fiber multilayer diagonal loom,” IEEE Access, vol. 5, pp. 15326–15331, August 2017.CrossRefGoogle Scholar
  2. [2]
    Y. Z. Wang, H. Xu, G. Drozdov, and D. Traian, “Mesoscopic friction and network morphology control the mechanics and processing of carbon nanotube yarns,” Carbon, vol. 139, pp. 94–104, November 2018.CrossRefGoogle Scholar
  3. [3]
    A. Sengab and R. C. Picu, “Mechanical behavior of carbon nanotube yarns with stochastic micro-structure obtained by stretching buckypaper,” Composites Science and Technology, vol. 166, pp. 54–65, September 2018.CrossRefGoogle Scholar
  4. [4]
    J. C. Anike, K. Belay, and J. L. Abot, “Piezoresis-tive response of carbon nanotube yarns under tension: parametric effects and phenomenology,” New Carbon Materials, vol. 33, no. 2, pp. 140–154, April 2018.CrossRefGoogle Scholar
  5. [5]
    Y. Y. Chu and X. G. Chen, “Finite element modelling effects of inter-yarn friction on the single-layer highperformance fabrics subject to ballistic impact,” Mechanics of Materials, vol. 126, pp. 99–110, November 2018.CrossRefGoogle Scholar
  6. [6]
    G. Nilakantan and J. W. Gillespie, “Yarn pull-out behavior of plain woven Kevlar fabrics: Effect of yarn sizing, pullout rate, and fabric pre-tension,” Composite Structures, vol. 101, pp. 215–224, July 2013.CrossRefGoogle Scholar
  7. [7]
    A. R. Labanieh, C. Garnier, P. Ouagne, O. Dalverny, and D. Soulat, “Intra-ply yarn sliding defect in hemisphere preforming of a woven preform,” Composites Part A: Applied Science and Manufacturing, vol. 107, pp. 432–446, April 2018.CrossRefGoogle Scholar
  8. [8]
    H. Kim and S. J. Kim, “High toughness of bio-inspired multistrand coiled carbon nanotube yarn,” Carbon, vol. 131, pp. 60–65, May 2018.CrossRefGoogle Scholar
  9. [9]
    S. Hong, M. Minary-Jolandan, and M. Naraghi, “Controlling the wettability and adhesion of carbon fibers with polymer interfaces via grafted nanofibers,” Composites Science and Technology, vol. 117, pp. 130–138, September 2015.CrossRefGoogle Scholar
  10. [10]
    S. Kim and G. J. Vachtsevanos, “An intelligent approach to integration and control of textile processes,” Information Sciences, vol. 123, no. 3, pp. 181–199, April 2000.CrossRefGoogle Scholar
  11. [11]
    T. Yan, Z. Wang, Y. Q. Wang, and Z. J. Pan, “Carbon/ graphene composite nanofiber yarns for highly sensitive strain sensors,” Materials Design, vol. 143, pp. 214–223, April 2018.CrossRefGoogle Scholar
  12. [12]
    Y. Y. Chu, S. N. Min, and X. G. Chen, “Numerical study of inter-yarn friction on the failure of fabrics upon ballistic impacts,” Materials and Design, vol. 115, pp. 299–316, February 2017.CrossRefGoogle Scholar
  13. [13]
    H. Liang, L. Zhang, H. R. Karimi, and Q. Zhou, “Fault estimation for a class of nonlinear semi-Markovian jump systems with partly unknown transition rates and output quantization,” International Journal of Robust and Nonlinear Control, vol. 28, no. 18, pp. 5962–5980, October 2018.MathSciNetCrossRefzbMATHGoogle Scholar
  14. [14]
    Y. Zhang, J. Sun, H. Liang, and H. Li, “Event-triggered adaptive tracking control for multi-agent systems with unknown disturbances,” IEEE Transactions on Cybernetics, pp. 1–12, 2018. DOI: 10.1109/TCYB.2018.2869084Google Scholar
  15. [15]
    H. Ma, H. Liang, H.J. Ma, and Q. Zhou, “Nussbaum gain adaptive backstepping control of nonlinear strict-feedback systems with unmodeled dynamics and unknown deadzone,” International Journal of Robust and Nonlinear Control, vol. 28, no. 17, pp. 5326–5343, September 2018.MathSciNetCrossRefGoogle Scholar
  16. [16]
    N. Adhikary and C. Mahanta, “Sliding mode control of position commanded robot manipulators,” Control Engineering Practice, vol. 81, pp. 183–198, December 2018.CrossRefGoogle Scholar
  17. [17]
    L. Cao, H. Li and Q. Zhou, “Adaptive intelligent control for nonlinear strict-feedback systems with virtual control coefficients and uncertain disturbances Based on eventtriggered mechanism,” IEEE Transactions on Cybernetics, vol. 48, no.12, pp. 3390–3402, December 2018.CrossRefGoogle Scholar
  18. [18]
    H. Li, Y. Wang, D. Yao, and R. Lu, “A sliding mode approach to stabilization of nonlinear Markovian jump singularly perturbed systems,” Automatica, vol. 97, pp. 404–413, November 2018.MathSciNetCrossRefGoogle Scholar
  19. [19]
    Y. Zhang, H. J. Liang, H. Ma, Q. Zhou, and Z. D. Yu, “Distributed adaptive consensus tracking control for nonlinear multi-agent systems with state constraints,” Applied Mathematics and Computation, vol. 326, pp. 16–32, June 2018.MathSciNetCrossRefGoogle Scholar
  20. [20]
    H. Ma, Q. Zhou, L. Bai, and H. Liang, “Observerbased adaptive fuzzy fault-tolerant control for stochastic nonstrict-feedback nonlinear systems with input quantization,” IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. 49, no. 2, pp. 287–298, Feb 2019.CrossRefGoogle Scholar
  21. [21]
    H. Ma, H. Liang, Q. Zhou, and C. K. Ahn, “Adaptive dynamic surface control design for uncertain nonlinear strict-feedback systems with unknown control direction and disturbances,” IEEE Transactions on Systems, Man and Cybernetics: Systems, pp. 1–10, 2018. DOI: 10.1109/TSMC.2018.2855170Google Scholar
  22. [22]
    G. P. Incremona, M. Rubagotti, and A. Ferrara, “Sliding mode control of constrained nonlinear systems,” IEEE Transactions on Automatic Control, vol. 62, no. 6, pp. 2965–2972, June 2017.MathSciNetCrossRefzbMATHGoogle Scholar
  23. [23]
    J. Yang, S. Li, and X. Yu, “Sliding-mode control for systems with mismatched uncertainties via a dis-turbance observer,” IEEE Trans. Ind. Electron., vol. 60, no. 1, pp. 160–169, January 2013.CrossRefGoogle Scholar
  24. [24]
    D. Ginoya, P. D. Shendge, and S. B. Phadke, “Sliding mode control for mismatched uncertain systems using an extended disturbance observer,” IEEE Trans. Ind. Electron., vol. 61, no. 4, pp. 1983–1992, April 2014.CrossRefGoogle Scholar
  25. [25]
    B. Wang, G. L. Ma, D. Xu, L. Zhang, and J. H. Zhou, “Switching sliding-mode control strategy based on multitype restrictive condition for voltage control of buck converter in auxiliary energy source,” Applied Energy, vol. 228, pp. 1373–1384, October 2018.CrossRefGoogle Scholar
  26. [26]
    J. H. Li and Q. L. Zhang, “A linear switching function approach to sliding mode control and observation of descriptor systems,” Automatica, vol. 95, pp. 112–121, September 2018.MathSciNetCrossRefzbMATHGoogle Scholar
  27. [27]
    L. J. Zheng, F. Y. Jiang, J. C. Song, Y. G. Gao, and M. Q. Tian, “A discrete-time repetitive siding mode control for voltage source inverters,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 6, no. 3, pp. 1553–1566, September 2018.CrossRefGoogle Scholar
  28. [28]
    Y. Y. Wang, Y. B. Gao, H. R. Karimi, H. Shen, and Z. J. Fang, “Sliding mode control of fuzzy singularly perturbed systems with application to electric circuit,” IEEE Transactions on Systems Man Cybernetics-systems, vol. 48, no. 10, pp. 1667–1675, October 2018.CrossRefGoogle Scholar
  29. [29]
    Y. Y. Wang, H. Shen, H. R. Karimi, and D. P. Duan, “Dissipativity-based fuzzy integral sliding mode control of continuous-time T-S fuzzy systems,” IEEE Transactions on Fuzzy Systems, vol. 26, no. 3, pp. 1164–1176, June 2018.CrossRefGoogle Scholar
  30. [30]
    Y. Y. Wang, Y. Q. Xia, H. Y. Li, and P. F. Zhou, “A new integral sliding mode design method for nonlinear stochastic systems,” Automatica, vol. 90, pp. 304–309, April 2018.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© ICROS, KIEE and Springer 2019

Authors and Affiliations

  1. 1.School of Electrical Engineering and Automation, and Key Laboratory of Advanced Electrical Engineering and Energy TechnologyTianjin Polytechnic UniversityTianjinChina
  2. 2.School of Electrical Engineering and AutomationTianjin Polytechnic UniversityTianjinChina
  3. 3.School of Mechanical EngineeringTianjin Polytechnic UniversityTianjinChina

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