Deep Learning and Model Predictive Control for Self-Tuning Mode-Locked Lasers
This work addresses the need for intelligent self-tuning in optical systems like mode-locked lasers, which is incremental as it combines existing methods (DL and MPC) for a specific application.
The authors tackled the problem of self-tuning mode-locked fiber lasers by integrating deep learning with model predictive control to approximate unknown fiber birefringence and maintain robust, high-energy pulses, demonstrating effectiveness on a specific laser system.
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.