关键词: 无人机;自适应控制器;神经模糊推理算法;电推进;燃料电池;供气系统;性能优化

Abstract: Aiming at the application of hybrid electric propulsion system of an UAV based on polymer exchange membrane fuel cell and lithium-ion battery， a power management system control technology based on adaptive neuro-fuzzy inference system was researched and developed to control the hybrid electric propulsion system and optimize the performance of the fuel cell gas supply system. Taking the mathematical model of the hybrid electric propulsion system of an UAV as the research object， the relationship between the fuel cell current and the compressor power of the fuel cell gas supply system was studied， and the reference model of the relationship between the fuel cell current and the optimal compressor power was established. On the basis of the reference model， an adaptive controller was introduced to optimize the performance of the fuel cell gas supply system. The controller based on adaptive neuro-fuzzy inference system dynamically adjusts the actual operating power of the compressor to the optimal value defined in the reference model. The on-line learning and training ability of the adaptive controller was used to identify the nonlinear variation of the fuel cell current and generate the control signal of the compressor motor voltage to optimize the performance of the fuel cell gas supply system. The proton exchange membrane fuel cell （PEMFC） and lithium-ion hybrid electric propulsion system model was developed in Matlab simulation environment， and the designed controller was simulated and analyzed. The results show that the controller based on adaptive neuro-fuzzy inference system provides a novel and comprehensive way to optimize the performance of the compressor in the fuel cell gas supply system， and enables the fuel cell gas supply system to obtain the maximum net power output. The net power output of the fuel cell system was compared with the optimal compressor power and the constant compressor power. The results show that the optimized compressor power configuration saves 2.62% more energy than the constant compressor power configuration. At the same time， the fuel cell adaptive neuro-fuzzy inference system controller optimizes the energy utilization of the fuel cell gas supply system.

Key words: Unmanned aerial vehicles;Adaptive controller;Neuro-fuzzy inference algorithm;Electric propulsion;Fuel cell;Gas supply system;Performance optimization