Self-Tuning Fiber Lasers

Abstract

We demonstrate that the integration of data-driven machine learning strategies with adaptive control are capable of producing an efficient and optimal self-tuning algorithm for mode-locked fiber lasers. The adaptive controller, based upon a multiparameter extremum-seeking control algorithm, is capable of obtaining and maintaining high-energy, single-pulse states in a mode-locked fiber laser while the machine learning characterizes the cavity itself for rapid state identification and improved optimization. The theory developed is demonstrated on a nonlinear-polarization-rotation-based laser using waveplate and polarizer angles to achieve optimal passive mode-locking despite large disturbances to the system. The physically realizable objective function introduced divides the energy output by the fourth moment of the pulse spectrum, thus balancing the total energy with the time duration of the mode-locked solution. Moreover, its peaks are high-energy mode-locked states that have a safety margin near parameter regimes where mode-locking breaks down or the multipulsing instability occurs. The methods demonstrated can be implemented broadly to optical systems, or more generally to any self-tuning complex systems.