Adaptive neuro fuzzy inference system-based intelligent power management strategies for fuel cell/battery driven unmanned electric aerial vehicle

Intelligent power management (IPM) strategy is presented to control the fuel cell (FC)/battery-powered hybrid unmanned electrical aerial vehicle propulsion system. The IPM system shares the propulsion power demand between two power sources, by allowing the FC system to operate in maximum net power output. The IPM system, which manages FC system power output according to battery state of charge and load power variations, is based on the adaptive neuro fuzzy inference system (ANFIS). This power controller optimizes compressor motor voltage as a function of FC stack current density. The ANFIS-based controller estimation parameters, mean ((x) over bar), variance (sigma), and fuzzy output ((z) over bar), are updated at each time step to achieve reference model optimum values. In ANFIS power control architecture, the battery supplies the extra power needed by the propulsion system during the aircraft take-off period and starts to charge at the cruising period. ANFIS-based power control topology is compared with a static feedforward controller to identify the power handling characteristics of the IPM system.