An Improved Performance of Fuzzy logic Based Controller Design for Bioreactor Process Plant



Abstract Views
399

Downloads
380

Citations

Share

##plugins.themes.bootstrap3.article.sidebar##

Submitted Sep 9, 2019
Published Sep 26, 2019
10.24018/ejeng.2019.4.9.1532

Abstract viewed = 399 times

Table of Contents

##plugins.themes.bootstrap3.article.main##

Proportional Integral Derivative, PID control is one of the earlier control strategies and most widely used controller for industries. A nonlinear technique, Mamdani structure was used for the bioreactor Simulink model. The fuzzy logic and its structure of the controller were adopted to set the fuzzy logic input member function. The fuzzy logic controller automatically tuned the PID controller, using an existing complex mapping function to generate the suitable values for the bioreactor process plant.

Keywords: Fuzzy Logic PID Controller Bioreactor Delay Time

Downloads

Download data is not yet available.

References

  1. Ghasem D. N, Biochemical Engineering and Biotechnology, 2nd ed. Chichester, U.K.: Elsevier, 2015, pp. 193 - 226.
     Google Scholar
  2. Bueno E, Bilgen .B. Carrier .R .L, “Increased rate of chondrocyte aggregation in a wavy-walled bioreactor,” Biotechnology and Bioengineering Journal, vol. 88, no. 6, pp. 767-777, Jan. 2004.
     Google Scholar
  3. H. C. Chen, Y. C. Hu, “Bioreactors for tissue engineering,” Biotechnology letters Trans, vol. 28, no. 18, pp. 1415-1423, 2006.
     Google Scholar
  4. Dingyu .X, Yang Q. C, P. A. Darek linear Feedback Control: The Society for Industrial and Applied Mathematics, 1st ed. Chichester, U.K.: Wiley, 2007.
     Google Scholar
  5. L. A. Zadeh, “Outline of a New Approach to the analysis of complex systems and decision processes,” IEEE Transactions on systems, Man, and Cybernetics, 28-44, 1973.
     Google Scholar
  6. Bellman R, Zadeh L, “Decision-making in Fuzzy environment,” Journal of Management Science, vol. 17, no. 04, pp. 164-168, 2006.
     Google Scholar
  7. Wang L .X, J. M. Mendel, “Generating Fuzzy Rules by Learning from Examples,” IEEE Transactions on systems, Man, and Cybernetics, vol. 22, no. 06, pp. 122 - 127, 1992.
     Google Scholar
  8. Kasahara .M, T. Yamazaki, T. Kuzu, Y. Hashimoto, “Stability Analysis and Tuning of PID Controller in VAV Systems,” ASHRAE Transaction, vol. 107, no. 01, pp. 221 - 229, 2001.
     Google Scholar
  9. Sangram Mehetab, “Study of the Design and Tuning Methods OF PID Controller based on Fuzzy Logic and Genetic Algorithm,” IEEE Transactions on systems, Man, and Cybernetics, vol. 17, no. 04, pp. 152 - 161, 2011.
     Google Scholar
  10. Satya S, O. Gupta “New Techniques of PID Controller Tuning of a DC Motor-Development of a Toolbox,” in MIT International Journal of Electrical and Instrumentation Engineering, vol. 02, no. 05, pp. 65-69, 2012.
     Google Scholar
  11. Ogata K, Modern Control Engineering, 2nd ed. Prentice Hall, NJ, 1990.
     Google Scholar