Control of Multistability in an Erbium-Doped Fiber Laser by an Artificial Neural Network: A Numerical Approach

Abstract

A recurrent wavelet first-order neural network (RWFONN) is proposed to select a desired attractor in a multistable erbium-doped fiber laser (EDFL). A filtered error algorithm is used to classify coexisting EDFL states and train RWFONN. The design of the intracavity laser power controller is developed according to the RWFONN states with the block control linearization technique and the super-twisting control algorithm. Closed-loop stability analysis is performed using the boundedness of synaptic weights. The efficiency of the control method is demonstrated through numerical simulations.

Keywords

• artificial neural network
• erbium-doped fiber laser
• recurrent wavelet first-order neural network
• filtered error algorithm
• block control linearization technique
• super-twisting control algorithm