Control of Multistability in an Erbium-Doped Fiber Laser by an Artificial Neural Network: A Numerical Approach

Daniel A. Magallón, Rider Jaimes-Reátegui (), Juan H. García-López (), Guillermo Huerta-Cuellar, Didier López-Mancilla and Alexander N. Pisarchik
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Daniel A. Magallón: Optics, Complex Systems and Innovation Laboratory, Centro Universitario de los Lagos, Universidad de Guadalajara, Enrique Díaz de León 1144, Colonia Paseos de la Montaña, Lagos de Moreno 47463, Mexico
Rider Jaimes-Reátegui: Optics, Complex Systems and Innovation Laboratory, Centro Universitario de los Lagos, Universidad de Guadalajara, Enrique Díaz de León 1144, Colonia Paseos de la Montaña, Lagos de Moreno 47463, Mexico
Juan H. García-López: Optics, Complex Systems and Innovation Laboratory, Centro Universitario de los Lagos, Universidad de Guadalajara, Enrique Díaz de León 1144, Colonia Paseos de la Montaña, Lagos de Moreno 47463, Mexico
Guillermo Huerta-Cuellar: Optics, Complex Systems and Innovation Laboratory, Centro Universitario de los Lagos, Universidad de Guadalajara, Enrique Díaz de León 1144, Colonia Paseos de la Montaña, Lagos de Moreno 47463, Mexico
Didier López-Mancilla: Optics, Complex Systems and Innovation Laboratory, Centro Universitario de los Lagos, Universidad de Guadalajara, Enrique Díaz de León 1144, Colonia Paseos de la Montaña, Lagos de Moreno 47463, Mexico
Alexander N. Pisarchik: Center for Biomedical Technology, Campus Montegancedo, Technical University of Madrid, 28223 Madrid, Spain

Mathematics, 2022, vol. 10, issue 17, 1-20

Abstract: A recurrent wavelet first-order neural network (RWFONN) is proposed to select a desired attractor in a multistable erbium-doped fiber laser (EDFL). A filtered error algorithm is used to classify coexisting EDFL states and train RWFONN. The design of the intracavity laser power controller is developed according to the RWFONN states with the block control linearization technique and the super-twisting control algorithm. Closed-loop stability analysis is performed using the boundedness of synaptic weights. The efficiency of the control method is demonstrated through numerical simulations.

Keywords: artificial neural network; erbium-doped fiber laser; recurrent wavelet first-order neural network; filtered error algorithm; block control linearization technique; super-twisting control algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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