Machine Learning Methods for Control of Fibre Lasers with Double Gain Nonlinear Loop Mirror

Kokhanovskiy, Alexey, Ivanenko, Aleksey, Kobtsev, Sergey, Smirnov, Sergey and Turitsyn, Sergey (2019). Machine Learning Methods for Control of Fibre Lasers with Double Gain Nonlinear Loop Mirror. Scientific Reports, 9 (1),

Abstract

Many types of modern lasers feature nonlinear properties, which makes controlling their operation a challenging engineering problem. In particular, fibre lasers present both high-performance devices that are already used for diverse industrial applications, but also interesting and not yet fully understood nonlinear systems. Fibre laser systems operating at high power often have multiple equilibrium states, and this produces complications with the reproducibility and management of such devices. Self-tuning and feedback-enabled machine learning approaches might define a new era in laser science and technology. The present study is the first to demonstrate experimentally the application of machine learning algorithms for control of the pulsed regimes in an all-normal dispersion, figure-eight fibre laser with two independent amplifying fibre loops. The ability to control the laser operation state by electronically varying two drive currents makes this scheme particularly attractive for implementing machine learning approaches. The self-tuning adjustment of two independent gain levels in the laser cavity enables generation-on-demand pulses with different duration, energy, spectral characteristics and time coherence. We introduce and evaluate the application of several objective functions related to selection of the pulse duration, energy and degree of temporal coherence of the radiation. Our results open up the possibility for new designs of pulsed fibre lasers with robust electronics-managed control.

Publication DOI: https://doi.org/10.1038/s41598-019-39759-1
Divisions: Engineering & Applied Sciences > Electrical, electronic & power engineering
Engineering & Applied Sciences > Institute of Photonics
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Uncontrolled Keywords: General
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.nat ... 598-019-39759-1 (Publisher URL)
Published Date: 2019-02-27
Authors: Kokhanovskiy, Alexey
Ivanenko, Aleksey
Kobtsev, Sergey
Smirnov, Sergey
Turitsyn, Sergey ( 0000-0003-0101-3834)

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