Abstract
Fuzzy logic control is an increasingly popular technique in the past decades since it has a linguistic based structure and its performance is quite robust for nonlinear systems. For many real-world control problems, it is possible to find a working Fuzzy Logic Controller (FLC) by formulating heuristic knowledge and by using a “trial and error” approach for fine-tuning. This may not, however, always yield the anticipated results and is undoubtedly a tedious task because of the huge number of tuning parameters involved. To overcome this problem, a number of advanced approaches have been reported in the literature. This present work deals with optimization of a fuzzy logic controller with the help of genetic algorithm to control the liquid level of a tank. The fuzzy logic model developed by Takagi-Sugeno (T-S) has been used here. The parameters of T-S type fuzzy logic controller have been optimized within a defined range using genetic algorithm, and the results are discussed here.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
D. Su, K. Ren, J. Luo, C. He, L. Wang, X. Zhang, Programmed and simulation of the fuzzy control list in fuzzy control, in IEEE/WCICA, 2010, pp. 1935–1940
H. Du, N. Zhang, Application of evolving Takagi-Sugeno fuzzy model to nonlinear system identification. Appl. Soft Comput. 8(1), 676–686 (2008)
M. Männle, FTSM-fast Takagi-Sugeno fuzzy modeling, in Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS’00) (Budapest, Romaina, 2000), pp. 663–668
Y.-C. Chiou, L.W. Lan, Genetic fuzzy logic controller: an iterative evolution algorithm with new encoding method. Fuzzy Sets Syst. 152(3), 617–635 (2005)
K.C. Ng, Y. Li, Design of sophisticated fuzzy logic controllers using genetic algorithms, in Fuzzy Systems. Proceedings of the IEEE World Congress on Computational Intelligence, 1994
S. Khan et al., Design and implementation of an optimal fuzzy logic controller using genetic algorithm. J. Comput. Sci. 4(10), 799–806 (2008)
S. Balochian, E. Ebrahimi, Parameter optimization via cuckoo optimization algorithm of fuzzy controller for liquid level control. J. Eng. (2013)
Francisco Herrera, Manuel Lozano, Jose L. Verdegay, Tuning fuzzy logic controllers by genetic algorithms. Int. J. Approximate Reasoning 12(3-4), 299–315 (1995)
L.A. Zadeh, Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. 1, 28–44 (1973)
H. Roubos, S. Magne, Compact fuzzy models and classifiers through model reduction and evolutionary optimization, in The Practical Handbook of Genetic Algorithms: Applications (Chapman & Hall/CRC 2001)
D.E. Goldberg, Genetic algorithms in search, optimization and machine learning (Addison-Wesley, Reading, 1989)
J.H. Holland, Adaptation in natural and artificial systems (University of Michigan Press, Ann Arbor, 1975)
E.A. Elayan, Design of heuristic fuzzy logic controller for liquid level control, in International Conference on Intelligent System Modelling and Simulation, 2014
S. Balochian, E. Ebrahimi, Parameter optimization via cuckoo optimization algorithm of fuzzy controller for liquid level control. J. Eng. 2013
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ganguly, P., Kalam, A., Zayegh, A. (2019). Modelling of an Optimum Fuzzy Logic Controller Using Genetic Algorithm. In: Chattopadhyay, S., Roy, T., Sengupta, S., Berger-Vachon, C. (eds) Modelling and Simulation in Science, Technology and Engineering Mathematics. MS-17 2017. Advances in Intelligent Systems and Computing, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-74808-5_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-74808-5_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74807-8
Online ISBN: 978-3-319-74808-5
eBook Packages: EngineeringEngineering (R0)