Optimized Fuzzy Control by Particle Swarm Optimization Technique for Control of CSTR

Authors: Saeed Vaneshani, Hooshang Jazayeri-Rad

Abstract:

Fuzzy logic control (FLC) systems have been tested in many technical and industrial applications as a useful modeling tool that can handle the uncertainties and nonlinearities of modern control systems. The main drawback of the FLC methodologies in the industrial environment is challenging for selecting the number of optimum tuning parameters. In this paper, a method has been proposed for finding the optimum membership functions of a fuzzy system using particle swarm optimization (PSO) algorithm. A synthetic algorithm combined from fuzzy logic control and PSO algorithm is used to design a controller for a continuous stirred tank reactor (CSTR) with the aim of achieving the accurate and acceptable desired results. To exhibit the effectiveness of proposed algorithm, it is used to optimize the Gaussian membership functions of the fuzzy model of a nonlinear CSTR system as a case study. It is clearly proved that the optimized membership functions (MFs) provided better performance than a fuzzy model for the same system, when the MFs were heuristically defined.

Keywords: continuous stirred tank reactor (CSTR), fuzzy logiccontrol (FLC), membership function(MF), particle swarmoptimization (PSO)

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328342

References:

[1] Eberhart, R. and Kennedy, I."A new optimizer using particle swarm theory". Symposium on Micro Machine and Human Science, 39-43, 1995.
[2] J. Kennedy and R.C. Eberhart, "Particle swarm optimization ", Proceeding of the 1995 IEEE International Conference on Neural Networks (Perth, Australia), IEEE Service Centre, 1995.
[3] Hong, T. P., & Lee, C. , "Induction of fuzzy rules and membership functions from training examples". Fuzzy Sets and Systems, 84(1), 33- 47, 1996.
[4] Babuska R and Verbruggen H. B. "An Overview of Fuzzy modeling for Control, Control Engineering Practice", vol 4 No 11, 1996 .
[5] Lee Chuen Chien:" Fuzzy Logic in Control Systems", Fuzzy Logic Controller - Part I. IEEE Transcations on Systems, Man and Cybernetics. Vol 20 No 2, March/April, 1990.
[6] J. Yen, R. Langari: "Fuzzy Logic: Intelligence, Control, and Information", Prentice-Hall, 1999.
[7] Zadeh, L. A.," Fuzzy Sets, Information Control", 330-353, 1965.
[8] Mamdani E. H.," Application of Fuzzy Algorithms for Control of Simple Dynamic Plants", IEE Proceed, 121(3) 585-588, 1974.
[9] King. P. J., and Mamdani, E. H. "The Application of Fuzzy Control Systems to Industrial Processes", Automatica, 13(3) 25-242, 1977.
[10] Luis E. Zarate, Peterson Resende, & M. Benjamin, "A Fuzzy Logic and Variable Structure Base Controller for CSTR Control". Annual conference of IEEE, 2001.
[11] M. Nikravesh ,A.E. Farell,T.G. Stanford, "Control of nonisothermal CSTR with time varying parameters via dynamic neural network control (DNNC)", Chemical Engineering Journal, vol 76,pp.1-16,2000.
[12] Esmin, A. A. " 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. 2007.
[13] Farhad Aslam, Gagandeep Kaur." 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, 2011.
[14] G. Coath and S. Halgamuge. "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, 2003.