Autors: Yordanova, S. T.
Title: Industrial Design of Type-1 and Interval Type-2 Fuzzy Logic Control
Keywords: Controller design, Programmable logic controller implementation, Simulations, Takagi-Sugeno-Kang plant models, Type-1 and interval type-2 PID FLC

Abstract: This paper focuses on the design of type-1 and interval type-2 (IT2) PID fuzzy logic controllers (FLC) for ensuring-by a programmable logic controller (PLC)-a high-performance real-time liquid level control in a carbonization column (CCl) for soda production. Firstly, Takagi-Sugeno-Kang models-derived via genetic algorithms parameter optimizations, experimental data and simulations for the basic and the worst CCl loads-are studied at different operation points, and the worst Ziegler-Nichols (ZN) model is assessed. Next, two-input fuzzy units are designed-assuming various membership functions (MF) and uncertainties-and the greatest linearization gain is computed. Based on it and the ZN worst plant model, the parameters of the FLC input pre-processing differentiator and the PI post-processing are empirically tuned. Finally, a PLC oriented analytical description of the IT2 MF, fuzzy rules and type-reduction is suggested. The designed FLC systems are studied via simulation to determine the factors that have the greatest impact on the system performance improvement. The obtained results unveil that the tuned FLC outperforms the tuned linear PI. Better system performance is achieved by a small number of MF with large support ensuring economical PLC presentation.

References

  1. S. Ahmad, S. Ali, R. Tabasha, “The design and implementation of a fuzzy gain-scheduled PID controller for the Festo MPS PA compact workstation liquid level control,” Engineering Science and Technology, an International Journal, vol. 23, pp. 307–315, 2020, doi: 10.1016/j.jestch.2019.05.014.
  2. C. Chao, N. Sutarna, J. Chiou, C. Wang, "An optimal fuzzy PID controller design based on conventional PID control and nonlinear factors," Applied Sciences, vol. 9, no. 6, 1224, pp. 1-18, 2019, doi: 10.3390/app9061224.
  3. J. Jantzen, Foundations of Fuzzy Logic Control. A Practical Approach, The Atrium, Southern Gate, Chichester, West Sussex, John Wiley & Sons Ltd, 2013.
  4. A. Venkataraman, “Design and implementation of adaptive PID and adaptive fuzzy controllers for a level process station,” Advances in Technology Innovation, vol. 6, no. 2, pp. 90-105, 2021, doi: 10.46604/aiti.2021.6047.
  5. S. Yordanova, Design of Fuzzy Supervisor-based Adaptive Process Control Systems, In: New Approaches in Intelligent Control: Techniques, Methodologies and Applications, Book series “Intelligent Systems Reference Library”, Springer Int. Publishing, Switzerland, 2016.
  6. Z. Aydogmus, “A real-time robust fuzzy-based level control using programmable logic controller,” Elektronika ir Elektrotechnika, vol. 21, no. 1, pp. 13–17, 2015, doi: 10.5755/j01.eee.21.1.7812.
  7. F. Chabni, R. Taleb, A. Benbouali, M. Bouthiba, “The application of fuzzy control in water tank level using Arduino,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 4, pp. 261–265, 2016, doi: 10.14569/IJACSA.2016.070432.
  8. M. Sellitto, E. Balugani, R. Gamberini, B. Rimini, “A fuzzy logic control application to the cement industry,” IFAC-Papers OnLine, vol. 51, pp. 1542-1547, 2018, doi: 10.1016/j.ifacol.2018.08.277.
  9. S. Yordanova, M. Slavov, B. Gueorguiev, “Parallel distributed compensation for improvement of level control in carbonisation column for soda production,” Control Engineering Practice, vol. 71, pp. 53-60, 2018, doi: 10.1016/j.conengprac.2017.10.003.
  10. A. Shaout, S. Ahmad, D. Osborn, “Comparison of fuzzy logic control and model predictive control for a smart adaptive cruise control vehicle system,” Jordan Journal of Electrical Engineering, vol. 10, no. 1, pp. 27-47, 2024, doi: 10.5455/jjee.204-1687812578.
  11. E. Nasri, T. Jarou, J. Abdouni, Y. ElKoudia, “Enhancing energy reliability and balance with fuzzy logic controlled microgrid system,” Jordan Journal of Electrical Engineering, vol. 9, no. 4, pp. 591-606, 2023, doi: 10.5455/jjee.204-1680652201.
  12. D. Deshkar, M. Kuber, P. Parakhi, “Fuzzy logic guidance law with optimized membership functions,” International Journal of Computer Science and Informatics, vol. I, no. 2, 2011, doi: 10.47893/IJCSI.2012.1028.
  13. B. Kumar, R. Dhiman, “Optimization of PID controller for liquid level tank system using intelligent techniques,” Canadian Journal on Electrical and Electronics Engineering, vol. 2, no. 11, 2011, doi: 10.1016/j.procs.2016.05.025.
  14. X. Du, H. Ying, "Derivation and analysis of the analytical structures of the interval type-2 fuzzy-PI and PD controllers," IEEE Transactions on Fuzzy Systems, vol. 18, no. 4, pp. 802-814, 2010, doi: 10.1109/TFUZZ.2010.2049022.
  15. J. Mendel, H. Hagras, W. Tan, W. Melek, H. Ying, Introduction to Type-2 Fuzzy Logic Control: Theory and Applications, Hoboken, New Jersey: IEEE Press, John Wiley & Sons, 2014.
  16. R. Raj, B. Mohan, “General structure of interval type-2 fuzzy PI/PD controller of Takagi–Sugeno type,” Engineering Applications of Artificial Intelligence, vol. 87, p. 103273, 2020, doi: 10.1016/j.engappai.2019.103273.
  17. D. Wu, J. Mendel, "Designing practical interval type-2 fuzzy logic systems made simple," Proceedings of 2014 IEEE International Conference on Fuzzy Systems, 2014.
  18. D. Wu, J. Mendel, “Recommendations on designing practical interval type-2 fuzzy systems,” Engineering Applications of Artificial Intelligence, vol. 85, pp. 182–193, 2019, doi: 10.1016/j.engappai.2019.06.012.
  19. Type-2 Fuzzy Inference Systems, 2024, https://www.mathworks.com/help/fuzzy/type-2-fuzzy-inference-systems.html.
  20. Al-Mahturi, F. Santoso, M. Garratt, S. Anavatti, "An intelligent control of an inverted pendulum based on an adaptive interval type-2 fuzzy inference system," IEEE International Conference on Fuzzy Systems, 2019, doi: 10.1109/FUZZ-IEEE.2019.8858948.
  21. A. El-Nagar, M. El-Bardini, “Practical implementation for the interval type-2 fuzzy PID controller using a low-cost microcontroller,” Ain Shams Engineering Journal, vol. 5, pp. 475–487, 2014, doi: 10.1016/j.asej.2013.12.005.
  22. D. Wu, "An overview of alternative type-reduction approaches for reducing the computational cost of interval type-2 fuzzy logic controllers," IEEE International Conference on Fuzzy Systems, 2012, doi: 10.1109/FUZZ-IEEE.2012.6251242.
  23. M. Nie, W. Tan, “Towards an efficient type-reduction method for interval type-2 fuzzy logic systems,” IEEE International Conference on Fuzzy Systems, 2008, doi: 10.1109/FUZZY.2008.4630559.
  24. IEC 61131–Programmable Controllers, 1999, https://webstore.iec.ch/en/publication/4550.
  25. C. Thieme, Sodium Carbonates, In: Ullmann's Encyclopedia of Industrial Chemistry, Weinheim Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012.
  26. Fuzzy Logic Toolbox: User’s Guide for Use with MATLAB, 2018, https://www.mathworks.com/.
  27. S. Yordanova, M. Slavov, D. Stoitseva-Delicheva, “Load-bound fuzzy logic control of an industrial nonlinear plant,” International Scientific Conference on Computer Science, 2023, doi: 10.1109/COMSCI59259.2023.10315847.
  28. T. Neskov, S. Yordanova, I. Topalova, Process Control and Production Automation, Technical University of Sofia, Sofia, 2007.

Issue

Jordan Journal of Electrical Engineering, vol. 11, pp. 131-150, 2025, Jordan, https://doi.org/10.5455/jjee.204-1720610452

Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus