无人机增程式电推进系统双模糊能量管理策略仿真

为提高应用于无人机的增程式电推进系统能量利用效率，采用基于双层模糊控制的能量管理策略并使用遗传算法对控制参数进行优化，依据飞行动力学理论在仿真飞行工况中设置不同扰动，检验能量管理策略的抗飞行扰动效果。仿真结果表明:相比于基于多点逻辑门规则和比例积分微分PID（proportion integral differential）的能量管理策略，双模糊能量管理策略可使发动机运行平均燃油消耗率下降3.4%，整体燃油消耗量下降3.8%，电池使用量降低10.6%，发动机平均转速误差下降77.0%，面对突风扰动、复合扰动和连续紊流扰动时转速最大波动量分别降低71.4%、72.6%和46.7%。经过遗传算法优化后的双模糊能量管理策略相比优化前的控制参数，发动机平均转速误差下降6.6%，面对上述3种扰动时转速最大波动量分别降低12.8%、8.3%和39.4%。 

Simulation on dual-fuzzy energy management strategy of UAV extended range electric propulsion system

In order to improve the energy utilization efficiency of the extended-range electric propulsion system applied to unmanned aerial vehicle （UAV）s，an energy management strategy based on double-layer fuzzy control was adopted and genetic algorithms were used to optimize the control parameters，and different disturbances were set in the simulated flight conditions according to the flight dynamics theory to test the anti-flight disturbance effect of the energy management strategy.The simulation results showed that compared with the energy management strategy based on multi-point logic gate rules and PID （proportion integral differential），the dual-fuzzy energy management strategy can reduce the average fuel consumption rate of the engine by 3.4% and the overall fuel consumption by 3.8%；the battery usage was reduced by 10.6%，the average engine speed error was reduced by 77.0%，and the maximum speed fluctuations in the face of sudden wind disturbance，compound disturbance and continuous turbulence disturbance were reduced by 71.4%，72.6% and 46.7%，respectively.Compared with the control parameters before optimization，the dual-fuzzy energy management strategy optimized by genetic algorithm reduced the average engine speed error by 6.6%，and reduced the maximum speed fluctuations by 12.8%，8.3%，and 39.4%，respectively，in the face of the above-mentioned three kinds of disturbances.