Deep Learning and Model Predictive Control for Self-Tuning Mode-Locked Lasers

TL;DR

This paper presents a novel integration of deep learning with model predictive control to self-tune mode-locked fiber lasers, enabling robust, high-energy pulse generation despite stochastic birefringence variations.

Contribution

It introduces the first DL-MPC framework for self-tuning fiber lasers, capable of modeling unknown physics and maintaining performance under drift.

Findings

Successfully approximates fiber birefringence

Builds a dynamic model for laser control

Maintains high-energy pulses despite birefringence drift

Abstract

Self-tuning optical systems are of growing importance in technological applications such as mode-locked fiber lasers. Such self-tuning paradigms require {\em 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 {\em deep learning} (DL) architecture with {\em 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 which is mode-locked by nonlinear polarization rotation. The method advocated can be broadly applied to a variety of optical systems that require robust controllers.