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Fuzzy Logic Controller Parameter Optimization Using Metaheuristic Cuckoo Search Algorithm for a Magnetic Levitation System

1
Carrera en Electrónica, Automatización y Control, Universidad de las Fuerzas Armadas ESPE, Av. Gral. Rumiñahui s/n, Sangolquí 171-5-231B, Ecuador
2
Research group of Propagation, Electronic Control, and Networking (PROCONET), Universidad de las Fuerzas Armadas ESPE, Av. Gral. Rumiñahui s/n, Sangolquí 171-5-231B, Ecuador
3
Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas ESPE, Av. Gral. Rumiñahui s/n, Sangolquí 171-5-231B, Ecuador
4
WICOM-Energy Research Group, Universidad de las Fuerzas Armadas ESPE, Av. Gral. Rumiñahui s/n, Sangolquí 171-5-231B, Ecuador
5
Department of Electronics Engineering, Escuela Técnica Superior de Ingenieros de Telecomunicación de Barcelona, Universitat Politècnica de Catalunya. C. Jordi Girona 31, 08034 Barcelona, Spain
6
Faculty of Engineering Technology, Department of Electrical Engineering, KU Leuven, TC Ghent 9000 Ghent, Belgium
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(12), 2458; https://doi.org/10.3390/app9122458
Received: 18 April 2019 / Revised: 2 June 2019 / Accepted: 13 June 2019 / Published: 16 June 2019
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Abstract

The main benefits of fuzzy logic control (FLC) allow a qualitative knowledge of the desired system’s behavior to be included as IF-THEN linguistic rules for the control of dynamical systems where either an analytic model is not available or is too complex due, for instance, to the presence of nonlinear terms. The computational structure requires the definition of the FLC parameters namely, membership functions (MF) and a rule base (RB) defining the desired control policy. However, the optimization of the FLC parameters is generally carried out by means of a trial and error procedure or, more recently by using metaheuristic nature-inspired algorithms, for instance, particle swarm optimization, genetic algorithms, ant colony optimization, cuckoo search, etc. In this regard, the cuckoo search (CS) algorithm as one of the most promising and relatively recent developed nature-inspired algorithms, has been used to optimize FLC parameters in a limited variety of applications to determine the optimum FLC parameters of only the MF but not to the RB, as an extensive search in the literature has shown. In this paper, an optimization procedure based on the CS algorithm is presented to optimize all the parameters of the FLC, including the RB, and it is applied to a nonlinear magnetic levitation system. Comparative simulation results are provided to validate the features improvement of such an approach which can be extended to other FLC based control systems. View Full-Text
Keywords: fuzzy logic controller; meta-heuristics; cuckoo search algorithm; magnetic levitation system fuzzy logic controller; meta-heuristics; cuckoo search algorithm; magnetic levitation system
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

García-Gutiérrez, G.; Arcos-Aviles, D.; Carrera, E.V.; Guinjoan, F.; Motoasca, E.; Ayala, P.; Ibarra, A. Fuzzy Logic Controller Parameter Optimization Using Metaheuristic Cuckoo Search Algorithm for a Magnetic Levitation System. Appl. Sci. 2019, 9, 2458.

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