Related papers: The self-learning AI controller for adaptive power beaming with fiber-array laser transmitter system

The self-learning AI controller for adaptive power beaming with fiber-array laser transmitter system

URL: http://arxiv.org/abs/2204.05227v1
Date: Fri, 8 Apr 2022 16:24:49 GMT
Title: The self-learning AI controller for adaptive power beaming with fiber-array laser transmitter system
Authors: A.M. Vorontsov, G.A. Filimonov
Abstract summary: We consider adaptive power beaming with fiber-array laser transmitter system in presence of atmospheric turbulence. In this study an optimal control is synthesized by a deep neural network (DNN) using target-plane PVA sensor data as its input.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In this study we consider adaptive power beaming with fiber-array laser transmitter system in presence of atmospheric turbulence. For optimization of power transition through the atmosphere fiber-array is traditionally controlled by stochastic parallel gradient descent (SPGD) algorithm where control feedback is provided via radio frequency link by an optical-to-electrical power conversion sensor, attached to a cooperative target. The SPGD algorithm continuously and randomly perturbs voltages applied to fiber-array phase shifters and fiber tip positioners in order to maximize sensor signal, i.e. uses, so-called, "blind" optimization principle. In opposite to this approach a perspective artificially intelligent (AI) control systems for synthesis of optimal control can utilize various pupil- or target-plane data available for the analysis including wavefront sensor data, photo-voltaic array (PVA) data, other optical or atmospheric parameters, and potentially can eliminate well-known drawbacks of SPGD-based controllers. In this study an optimal control is synthesized by a deep neural network (DNN) using target-plane PVA sensor data as its input. A DNN training is occurred online in sync with control system operation and is performed by applying of small perturbations to DNN's outputs. This approach does not require initial DNN's pre-training as well as guarantees optimization of system performance in time. All theoretical results are verified by numerical experiments.

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