Soft Computing

, Volume 19, Issue 6, pp 1523–1540 | Cite as

A cytokine network-inspired cooperative control system for multi-stage stretching processes in fiber production

  • Tao Zhang
  • Yaochu Jin
  • Yongsheng Ding
  • Kuangrong Hao


The quality and stability of stretching processes during the polyacrylonitrile-based precursor preparation could have strong influence on the properties of carbon fibers and subsequent processes efficiency. The distributed units existing in the stretching processes pose a serious challenge to the cooperative regulation of the whole process to achieve a proper ratio distribution among the different stretching units, each of which having a specific technological characteristics. In this paper, a novel cytokine-network-based stretching network (CNSN) is proposed to achieve the desired ratio distribution and control performance in large production lines composed of interconnected yet distributed units organized as nodes in both spatial and temporal layers. Based on the mechanism of the multi-layered cytokine network for regulating the whole immune system in human bodies, a dynamical model of stretching network is suggested in order to describe interaction between stretching units. The model consists of a control allocation layer, a distributed controller layer and an actuator layer. An allocation algorithm based on quadratic optimization has also been successfully proposed to enhance the system’s ability to redistribute stretching ratios to ensure the quality requirement of the final fiber. Numerical results show that the CNSN outperforms traditional control strategies for the multi-stage stretching process in fiber production lines. As the main components of the proposed CNSN are very generic, it can easily be extended to other large-scale production lines having multiple interconnected processes.


PAN fiber production Stretching processes Artificial cytokine networks Stretching networks  Multi-layer regulation networks Control allocation Distributed systems 


  1. Abjadi NR, Soltain J, Askari J, Markadeh GRA (2008) Nonlinear sliding-mode control of a multi-motor web-winding system without tension control. IET Control Theory Appl 3(4):419–427CrossRefGoogle Scholar
  2. Bodson M (2002) Evaluation of optimization methods for control allocation. J Guid Control Dyn 25(4):703–711CrossRefGoogle Scholar
  3. Cao X-H, Cheng P, Chen J-M, Sun Y-X (2013) An online optimization approach for control and communication codesign in networked cyber-physical systems. IEEE Trans Ind Inf 9(1):439–450CrossRefGoogle Scholar
  4. Chen JC, Harrison IR (2002) Modification of polyacrylonitrile (PAN) carbon fiber precursor via post-spinning plasticization and stretching in dimethyl formamide (DMF). Carbon 40(1):25–45CrossRefGoogle Scholar
  5. Chen J, Cao X, Cheng P, Xiao Y, Sun Y (2010) Distributed collaborative control for industrial automation with wireless sensor and actuator networks. IEEE Trans Ind Electron 57(12):4219–4230CrossRefGoogle Scholar
  6. Chien T-L, Chen C-C, Huang C-J (2010) Feedback linearization control and its application to MIMO cancer immunotherapy. IEEE Trans Control Syst Technol 18(4):953–961CrossRefGoogle Scholar
  7. Ding Y-S, Liu B (2011) An intelligent bi-cooperative decoupling control approach based on modulation mechanism of internal environment in body. IEEE Trans Control Syst Technol 19(3):692–698CrossRefGoogle Scholar
  8. Ding Y-S, Liang X, Hao K-R, Wang H-P (2013) An intelligent cooperative decoupling controller for coagulation bath in polyacrylonitrile carbon fiber production. IEEE Trans Control Syst Techno 21(2):467–479CrossRefGoogle Scholar
  9. Farhy LS (2004) Modeling of oscillations of endocrine networks with feedback. Methods Enzymol 384:54–81CrossRefGoogle Scholar
  10. Farmer JD, Kauffman SA, Packard NH (1987) Adaptive dynamic networks as models for the immune system and autocatalytic sets. Ann N. Y. Acad Sci 504(1):118–131CrossRefGoogle Scholar
  11. Han JQ (2009) From PID to active disturbance rejection control. IEEE Trans Ind Electron 56(3):900–906CrossRefGoogle Scholar
  12. Hess RA, Wells SR (2003) Sliding mode control applied to reconfigurable flight control design. J Guid Control Dyn 26(3):452– 462Google Scholar
  13. Hone, A.: Modeling a cytokine network [C]. 2007 IEEE Symposium on Foundations of, Computational Intelligence, pp 389–393, April 2007Google Scholar
  14. Hu Y-F, Ding Y-S, Hao K-R (2012) An immune cooperative particle swarm optimization algorithm for fault-tolerant routing optimization in heterogeneous wireless sensor networks. Math Probl Eng 2012:1–19Google Scholar
  15. Hu Y-F, Ding Y-S, Hao K-R, Ren L-H, Han H (2014) An immune orthogonal learning particle swarm optimization algorithm for routing recovery of wireless sensor networks with mobile sink. Int J Syst Sci 45(3):337–350CrossRefzbMATHGoogle Scholar
  16. Jerne NK, Cocteau J (1984) Idiotypic networks and other preconceived ideas. Immunol Rev 79(1):5–24CrossRefGoogle Scholar
  17. Juang JG, Huang M-T, Liu W-K (2008) PID control using presearched genetic algorithms for a MIMO system. IEEE Trans Syst Man Cybern Part C Appl Rev 38(5):716–727CrossRefGoogle Scholar
  18. Koc H, Knittel D, de Mathelin M, Abba G (2002) Modeling and robust control of winding systems for elastic webs. IEEE Trans Control Syst Technol 10(2):197–208CrossRefGoogle Scholar
  19. Li P, Lam J (2013) Decentralized control of compartmental networks with h-infty tracking performance. IEEE Trans Ind Electron 60(2):546–553CrossRefGoogle Scholar
  20. Liang X, Ding Y-S, Ren L-H, Hao K-R, Wang H-P, Chen J-J (2012) A bio-inspired multilayered intelligent cooperative controller for stretching process of fiber production. IEEE Trans Syst Man cybern Part C Appl Rev 42(3):367–377Google Scholar
  21. Luan Vu TN, Lee M (2010) Multi-loop PI controller design based on the direct synthesis for interacting multi-time delay processes. Int Soc Autom Trans 49(1):79–86Google Scholar
  22. Mehraeen S, Jagannathan S (2011) Decentralized optimal control of a class of interconnected nonlinear discrete-time systems by using online Hamilton–Jacobi–Bellman formulation. IEEE Trans Neural Netw 22(11):1757–1769CrossRefGoogle Scholar
  23. Mitsantisuk C, Ohishi K, Katsura S (2012) Control of interaction force of twin direct-drive motor system using variable wire rope tension with multisensor integration. IEEE Trans Ind Electron 59(1):498–510CrossRefGoogle Scholar
  24. Nguyen TN, Su S, Nguyen H (2011) Robust neuro-sliding mode multivariable control strategy for powered wheelchairs. IEEE Trans Neural Syst Rehabil Eng 19(1):105–111CrossRefGoogle Scholar
  25. Pagilla PR, Siraskar NB, Dwivedula RV (2007) Decentralized control of web processing lines. IEEE Trans Control Syst Technol 15(1):106–117CrossRefGoogle Scholar
  26. Sedghi R, Farsani I, Shokufar A (2008) The effect of commercial polyacrylonitrile fibers characterizations on the produced carbon fibers properties. J Mater Process Technol 198(1–3):60–67CrossRefGoogle Scholar
  27. Skogestad S (2000) Self-optimizing control: The missing link between steady-state optimization and control. Comput Chem Eng 24(2):569–575CrossRefGoogle Scholar
  28. Tan L-J, Chen H-F, Pan D, Pan N (2008) Investigating the spin ability in the dry-jet wet spinning of PAN precursor fiber. J Appl Polym Sci 110:1997–2000CrossRefGoogle Scholar
  29. Tjonnas J, Johansen TA (2010) Stabilization of automotive vehicles using active steering and adaptive brake control allocation. IEEE Trans Control Syst Technol 18(3):545–558CrossRefGoogle Scholar
  30. Wai R-J (2003) Robust control for nonlinear motor-mechanism coupling system using wavelet neural network. IEEE Trans Syst Man Cybern Part B Cybern 33(3):489–497CrossRefGoogle Scholar
  31. Wang S, Chen Z-H, Ma W-J, Ma Q-S (2006) Influence of heat treatment on physicalchemical properties of PAN-based carbon fiber. Ceram Int 32(3):291–295Google Scholar
  32. Wang C-L (2012) Multivariable adaptive backstepping control: a norm estimation approach. IEEE Trans Autom Control 57(4):989–995CrossRefGoogle Scholar
  33. Wu Z-Z (2011) LMI-based Multivariable PID controller design and its application to the control of the surface shape of magnetic fluid deformable mirrors. IEEE Trans Control Syst Technol 19(4):717–729 Google Scholar
  34. Wuand ZZ, Iqbal A (2011) LMI-based multivariable PID controller design and its application to the control of the surface shape of magnetic fluid deformable mirrors. IEEE Trans Control Syst Technol 19(4):717–729Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Tao Zhang
    • 1
  • Yaochu Jin
    • 1
    • 2
  • Yongsheng Ding
    • 1
  • Kuangrong Hao
    • 1
  1. 1.College of Information Science and Technology, Engineering Research Center of Digitized Textile and Fashion Technology, Ministry of EducationDonghua UniversityShanghai China
  2. 2.Department of ComputingUniversity of SurreyGuildford UK

Personalised recommendations