Forecasting the State of Charging Batteries on Board the UAV on the Basis of Neuro-Fuzzy Network Using

The aim of the article is to develop methodological principles for predicting the condition of batteries on board unmanned aerial vehicles on the basis of a fuzzy network. A method for predicting the condition of batteries, adapted to the conditions and features of unmanned aerial vehicle use, has been developed. It consists in determining the load voltage, load current, solar current, battery temperature on board the unmanned aerial vehicle during its flight in real time and comparing the obtained data with the reference characteristics stored in the memory of the onboard unmanned aerial vehicle microcomputer. Based on the fuzzy logic in the support and decision-making system, the condition of the batteries is predicted to form a recommendation for the continuation or termination of the unmanned aerial vehicle flight task.