Journal
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA B-OPTICAL PHYSICS
Volume 35, Issue 3, Pages 617-626Publisher
Optica Publishing Group
DOI: 10.1364/JOSAB.35.000617
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Funding
- Air Force Office of Scientific Research (AFOSR) [FA9550-12-1-0253, FA9550-17-1-0200]
- Army Research Office Young Investigator Program [W911NF-17-1-0422]
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Self-tuning optical systems are of growing importance in technological applications such as mode-locked fiber lasers. Such self-tuning paradigms require intelligent algorithms capable of inferring approximate models of the underlying physics and discovering appropriate control laws in order to maintain robust performance for a given objective. In this work, we demonstrate the first integration of a deep-learning (DL) architecture with model predictive control (MPC) in order to self-tune a mode-locked fiber laser. Not only can our DL-MPC algorithmic architecture approximate the unknown fiber birefringence, it also builds a dynamical model of the laser and appropriate control law for maintaining robust, high-energy pulses despite a stochastically drifting birefringence. We demonstrate the effectiveness of this method on a fiber laser that is mode-locked by nonlinear polarization rotation. The method advocated can be broadly applied to a variety of optical systems that require robust controllers. (c) 2018 Optical Society of America.
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