Call for Paper - June 2022 Edition
IJCA solicits original research papers for the June 2022 Edition. Last date of manuscript submission is May 20, 2022. Read More

Nonlinear Control of a Chemical Plant Employing a Combination of Fuzzy Logic and Particle Swarm Optimization Techniques

International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 33 - Number 9
Year of Publication: 2011
Saeed Vaneshani
Hooshang Jazayeri-Rad

Saeed Vaneshani and Hooshang Jazayeri-Rad. Article: Nonlinear control of a chemical plant employing a combination of fuzzy logic and particle swarm optimization techniques. International Journal of Computer Applications 33(9):58-63, November 2011. Full text available. BibTeX

	author = {Saeed Vaneshani and Hooshang Jazayeri-Rad},
	title = {Article: Nonlinear control of a chemical plant employing a combination of fuzzy logic and particle swarm optimization techniques},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {33},
	number = {9},
	pages = {58-63},
	month = {November},
	note = {Full text available}


Fuzzy logic control (FLC) systems have been tested in numerous practical and industrial applications as an important modeling tool that can cope with the uncertainties and nonlinearities of current control systems. The key shortcoming of the FLC approaches in the industrial environment is the number of tuning parameters to be chosen. In this paper a technique has been offered for optimizing the membership functions of a fuzzy scheme using particle swarm optimization (PSO) algorithm. A mixture of fuzzy logic and PSO technique is employed to design a controller for a nonlinear chemical plant. To establish its efficiency, the proposed technique was employed to enhance the Gaussian membership functions of the fuzzy model of a nonlinear continuous stirred tank heater (CSTH); results show that the optimized membership functions (MFs) offered better performance than a fuzzy model for the same system when the MFs were heuristically described.


  • Eberhart, R. and Kennedy, I. 1995. A new optimizer using particle swarm theory. Symposium on Micro Machine and Human Science, 39-43.
  • J. Kennedy and R.C. 1995. Eberhart, Particle swarm optimization , Proceeding of the 1995 IEEE International Conference on Neural Networks (Perth, Australia), IEEE Service Centre.
  • Hong, T. P., & Lee, C. 1996. Induction of fuzzy rules and membership functions from training examples. Fuzzy Sets and Systems, 84(1), 33–47,
  • Babuska R and Verbruggen H. B. 1996. An Overview of Fuzzy modeling for Control, Control Engineering Practice, vol 4 No 11.
  • Lee Chuen Chien. 1990. Fuzzy Logic in Control Systems, Fuzzy Logic Controller – Part I. IEEE Transcations on Systems, Man and Cybernetics. Vol 20 No 2, March/April,
  • J. Yen, R. Langari. 1999. Fuzzy Logic: Intelligence, Control, and Information, Prentice-Hall.
  • Zadeh, L. A. 1974. Fuzzy Sets, Information Control, 330-353, Mamdani E. H., Application of Fuzzy Algorithms for Control of Simple Dynamic Plants, IEE Proceed, 121(3) 585-588,
  • King. P. J. 1977. and Mamdani, E. H. The Application of Fuzzy Control Systems to Industrial Processes, Automatica, 13(3) 25-242, 1977.
  • Thornhill, N.F. Patwardhan, S.C., Shah,S.L.The CSTH simulation website, online: article in press.
  • Z.L. Gaing: A Particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR System, IEEE Trans. Energy Conversion, Vol. 19, No. 2, June 2004,pp. 384 – 391
  • Esmin, A. 2007. Generating Fuzzy Rules from Examples Using the Particle Swarm Optimization Algorithm, Hybrid Intelligent Systems, 2007. HIS (2007). 7th International Conference on 17-19 Sept, 340 – 343.
  • Farhad Aslam, Gagandeep Kaur. 2011. Comparative analysis of conventional P, PI, PID and fuzzy logic controllers for the efficient control of concentration in CSTR. International journal of computer applications. Volume 17-No.6,
  • G. Coath and S. Halgamuge. 2003. A Comparison of Constraint-handling Methods for the Application of Particle Swarm Optimization to Constrained Nonlinear Optimization Problems. In Proceedings of IEEE Congress on Evolutionary Computation 2003 (CEC 2003), Canbella, Australia. pp. 2419-2425.