Skip to main content
SpringerLink
Log in

Neuro-adaptive fast terminal sliding mode control of the continuous polymerization reactor in the presence of unknown disturbances

  • Published:
International Journal of Dynamics and Control Aims and scope Submit manuscript

Abstract

This paper presents the design of fast terminal sliding mode control based on feed forward neural network (FTSMC\(+\)NN) for a polymerization reactor in the presence of unknown disturbances. The FTSMC+NN offers robust trajectory tracking of the monomer concentration and the reactor temperature. The NN is employed to approximate the unknown complex dynamics and the external disturbances. Additionally, in order to avoid the chattering phenomenon, a continuous reaching law together with an adaptive gain estimator is adopted. The proposed controller showcases superior performances compared to the SMC and proportional-integral-derivative (PID) controller.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Jonathan C, Greeves N, Warren SG (2012) Organic chemistry. Oxford University Press, Oxford

    Google Scholar 

  2. Allcock HR, Lampe FW, Mark JE (2003) Contemporary polymer chemistry, 3rd edn. Pearson Education, Inc (Pearson/Prentice Hall), Upper Saddle River

    Google Scholar 

  3. Levenspiel O (2002) The chemical reactor omnibook. Oregon State University Bookstores, Upper Saddle River

    Google Scholar 

  4. Kong J, Chen X (2019) Dynamic optimization of batch free radical polymerization with conditional modeling formulation through the adaptive smoothing strategy. Comput Chem Eng 120:15–29

    Article  Google Scholar 

  5. Richards JR, Congalidis JP (2006) Measurement and control of polymerization reactors. Comput Chem Eng 30:1447–1463

    Article  Google Scholar 

  6. McKeen LW (2008) 10-high temperature polymers. In: McKeen LW (ed) Effect of temperature and other factors on plastics and elastomers, 2nd edn. William Andrew Publishing, Norwich, Plastics Design Library, pp 503–550

    Chapter  Google Scholar 

  7. Florenzano FH, Strelitzki R, Reed WF (1998) Absolute, on-line monitoring of molar mass during polymerization reactions. Macromolecules 31(21):7226–7238

    Article  Google Scholar 

  8. McAfee T, Leonardi N, Montgomery R, Siqueira J, Zekoski T, Drenski MF, Reed WF (2016) Automatic control of polymer molecular weight during synthesis. Macromolecules 49(19):7170–7183

    Article  Google Scholar 

  9. Kreft Tomasz, Reed Wayne F (2009) Predictive control and verification of conversion kinetics and polymer molecular weight in semi-batch free radical homopolymer reactions. Eur Polym Journal 45(8):2288–2303

    Article  Google Scholar 

  10. Kreft T, Reed WF (2009) Predictive control of average composition and molecular weight distributions in semi-batch free radical copolymerization reactions. Macromolecules 42(15):5558–5565

    Article  Google Scholar 

  11. Leu Giuseppe, Baratti Roberto (2000) An extended Kalman filtering approach with a criterion to set its tuning parameters; application to a catalytic reactor. Comput Chem Eng 23(11–12):1839–1849

    Article  Google Scholar 

  12. Dochain D, Pauss A (1998) On-line estimation of microbial specific growth-rates: an illustrative case study. Can J Chem Eng 66(4):626–631

    Article  Google Scholar 

  13. Salas S, Ghadipasha N, Zhu W, Mcafee T, Zekoski T, Reed W, Romagnoli J (2018) Framework design for weight-average molecular weight control in semi-batch polymerization. Control Eng Pract 78:12–23

    Article  Google Scholar 

  14. Kupilik MJ, Vincent TL (2011) Estimation of biogas composition in a catalytic reactor via an extended kalman filter. In: 2011 IEEE international conference on control applications (CCA), pp 768–773

  15. Li R, Corripio AB, Henson MA, Kurtz MJ (2004) On-line state and parameter estimation of EPDM polymerization reactors using a hierarchical extended Kalman filter. J Process Control 14(8):837–852

    Article  Google Scholar 

  16. Park M-J, Hur S-M, Rhee H-K (2002) Online estimation and control of polymer quality in a copolymerizationreactor. AIChE J 48(5):1013–1021

    Article  Google Scholar 

  17. Cheng Z, Liu X (2015) Optimal online soft sensor for product quality monitoring in propylene polymerization process. Neurocomputing 149:1216–1224

    Article  Google Scholar 

  18. Bustos G, Ferramosca A, Godoy J, Gonzalez A (2016) Application of model predictive control suitable for closed-loop re-identification to a polymerization reactor. J Process Control 44:1–13

    Article  Google Scholar 

  19. Wu S, Jin Q, Zhang R, Zhang J, Gao F (2017) Improved design of constrained model predictive tracking control for batch processes against unknown uncertainties. ISA Trans 69:273–280

    Article  Google Scholar 

  20. BenAmor S, Doyle FJ, McFarlane R (2004) Polymer grade transition control using advanced real-time optimization software. J Process Control 14(4):349–364

    Article  Google Scholar 

  21. Nagy ZK, Braatz RD (2003) Robust nonlinear model predictive control of batch processes. AIChE J 49(7):1776–1786

    Article  Google Scholar 

  22. Lucia S, Finkler T, Engell S (2013) Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty. J Process Control 23(9):1306–1319

    Article  Google Scholar 

  23. Chen C-T (2012) A sliding mode control strategy for temperature trajectory tracking in batch processes. IFAC Proc Vol 45(15):644–649 8th IFAC Symposium on Advanced Control of Chemical Processes

  24. Narwekar K, Shah VA (2020) Temperature control using sliding mode control: An experimental approach. In: Tuba M, Akashe S, Joshi A (eds) Information and communication technology for sustainable development. Springer, Singapore, pp 531–538

    Chapter  Google Scholar 

  25. Kadu CB, Khandekar AA, Patil CY (2018) Sliding mode controller with state observer for tito systems with time delay. Int J Dyn Control 6(2):99–808

    MathSciNet  Google Scholar 

  26. Aguilar-López R, Maya-Yescas R (2005) State estimation for nonlinear systems under model uncertainties: a classof sliding-mode observers. J Process Control 15(3):363–370

    Article  Google Scholar 

  27. Rahman AF, Spurgeon SK, Yan XG (2011) Sliding mode observer based control for a continuous fermentation process. Trans Inst Meas Control 34(7):769–779

    Article  Google Scholar 

  28. Uçak K, Öke Günel G (2020) An adaptive sliding mode controller based on online support vector regression for nonlinear systems. Soft Comput 24(8):4623–4643

  29. Sinha A, Mishra RK (2018) Control of a nonlinear continuous stirred tank reactor via event triggered sliding modes. Chem Eng Sci 187:52–59

    Article  Google Scholar 

  30. Aumi S, Mhaskar P (2013) An adaptive data-based modeling approach for predictive control of batch systems. Chem Eng Sci 91:11–21

    Article  Google Scholar 

  31. Wu H, Chen Y Wang J (2017) Model-free output feedback control of molecular weight distribution. In: 2017 6th data driven control and learning systems (DDCLS), Chongqing, pp 473-478

  32. Yuan Ping, Zhang Bi, Mao Zhizhong (2017) A self-tuning control method for Wiener nonlinear systems and its application to process control problems. Chin J Chem Eng 25(2):193–201

    Article  Google Scholar 

  33. Kamesh R, Rani KY (2017) Novel formulation of adaptive MPC as EKF using ANN model: multiproduct semibatch polymerization reactor case study. IEEE Trans Neural Netw Learn Syst 28(12):3061–3073

    Article  MathSciNet  Google Scholar 

  34. Tiwari S, Sawant P, Rahman I (2019) Recursive orthogonal least squares based adaptive control of a polymerisation reactor. Indian Chem Eng 61(3):236–247

    Article  Google Scholar 

  35. Tronci S, Baratti R (2017) A gain-scheduling pi control based on neural networks. Complexity 2017, 8 p

  36. Li S, Li Y (2016) Model predictive control of an intensified continuous reactor using a neural network wiener model. Neurocomputing 185:93–104

    Article  Google Scholar 

  37. Congalidis JP, Richards JR, Ray WH (1989) Feedforward and feedback control of a solution copolymerization reactor. AIChE J 35(6):891–907

    Article  Google Scholar 

  38. Boukattaya M, Mezghani N, Damak T (2018) Adaptive nonsingular fast terminal sliding-mode control for the tracking problem of uncertain dynamical systems. ISA Trans 77:1–19

    Article  Google Scholar 

  39. Lewis FL, Yesildirak A, Jagannathan S (1998) Neural network control of robot manipulators and nonlinear systems. Taylor & Francis Inc, Bristol

    Google Scholar 

  40. Ramezani-al MR, Tavanaei Sereshki Z (2019) A novel adaptive sliding mode controller design for tracking problem of an AUV in the horizontal plane. Int J Dyn Control 7:679–689

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Systems Engineering Department, KFUPM, P. O. Box 5067, Dhahran, 31261, Saudi Arabia

    Magdi S. Mahmoud, Muhammad Maaruf & Sami El-Ferik

Authors
  1. Magdi S. Mahmoud
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Maaruf
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sami El-Ferik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Magdi S. Mahmoud.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mahmoud, M.S., Maaruf, M. & El-Ferik, S. Neuro-adaptive fast terminal sliding mode control of the continuous polymerization reactor in the presence of unknown disturbances. Int. J. Dynam. Control 9, 1167–1176 (2021). https://doi.org/10.1007/s40435-020-00731-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40435-020-00731-x

Keywords

Search

Navigation