ISSN :2582-9793

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

Original Research (Published On: 01-Mar-2023 )
The self-learning AI controller for adaptive power beaming with fiber-array laser transmitter system
DOI : 10.54364/AAIML.2023.1148

Artem Vorontsov and Grigorii Filimonov

Adv. Artif. Intell. Mach. Learn., 3 (1):731-759

Artem Vorontsov : Kaspersky Lab USA

Grigorii Filimonov : University of Dayton

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DOI: 10.54364/AAIML.2023.1148

Article History: Received on: 19-Jan-23, Accepted on: 14-Feb-23, Published on: 01-Mar-23

Corresponding Author: Artem Vorontsov

Email: artem7vorontsov@gmail.com

Citation: Artem Vorontsov (2023). The self-learning AI controller for adaptive power beaming with fiber-array laser transmitter system. Adv. Artif. Intell. Mach. Learn., 3 (1 ):731-759


Abstract

    

In this study we consider adaptive power beaming with a fiber-array laser transmitter system in presence of atmospheric turbulence. For optimization of power transition through the atmosphere a fiber-array is traditionally controlled by stochastic parallel gradient descent (SPGD) algorithm where control feedback is provided via a 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, the so-called, ``blind'' optimization principle.

By contrast to this approach a prospective 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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