Prognosis & Health Management for the prediction of UAV flight endurance

In this work, a Model-based Prognosis algorithm to predict the Flight Endurance (FE) and the Remaining Mission Time (RMT) for a class of multirotor UAVs powered by Lithium Polymer (Li-Po) batteries is presented. A Safety Voltage Threshold (SVT) in the State of Charge (SoC) of the battery is established before reaching of End of Discharge (EoD), and the SVT is defined taking into account the existing relationship between the voltage and the SoC of the battery. The FE is predicted by forecasting when the SVT is reached and the RMT is computed as the difference between the FE and the actual time. The prediction is developed in three sequential steps: 1) estimation of the battery SoC, 2) propagation and prediction of the estimated SoC in the future to reach the SVT, and 3) updating the prediction. The Model-based Prognosis algorithm is based on the mathematical model of the UAV propulsion system, which is made up by a set of BrushLess DC motors and a Li-Po battery. The effectiveness of the proposed algorithm was tested in simulation and the results obtained demonstrate the efficacy of the proposed method to accurately predict the FE and RMT during the development of a mission. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.