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Distributed Pressure Sensing-Based Flight Control for Small Fixed-Wing Unmanned Aerial Systems

机译:小型固定翼无人机系统基于压力感测的飞行控制

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Small fixed-wing unmanned aerial systems (UAS) may require increased agility when operating in turbulent wind fields. In these conditions, conventional sensor suites could be augmented with additional flow-sensing to extend the aircraft's usable flight envelope. Inspired by distributed sensor arrays in biological systems, a UAS with a chordwise array of pressure sensors was developed. Wind-tunnel testing characterized these sensors alongside a conventional airspeed sensor and an angle-of-attack (AoA) vane, and showed a single pressure measurement gave a linear response to AoA prestall. Flight tests initially manually piloted the vehicle through pitching maneuvers, then in a series of automated maneuvers based on closed-loop feedback using an estimate of AoA from the single pressure port. The AoA estimate was successfully used to control the attitude of the aircraft. An artificial neural network (ANN) was trained to estimate the AoA and airspeed using all pressure ports in the array, and validated using flight-trial data. The ANN more accurately estimated the AoA over a single-port method with good robustness to stall and unsteady flow. Distributed flow sensors could be used to supplement conventional flight control systems, providing enhanced information about wing flow conditions with application to systems with highly flexible or morphing wings.
机译:在湍流的风场中运行时,小型固定翼无人机系统(UAS)可能需要提高敏捷性。在这种情况下,常规传感器套件可以增加额外的流量感应功能,以扩展飞机的可用飞行范围。受生物系统中分布式传感器阵列的启发,开发了带有弦式压力传感器阵列的UAS。风洞测试将这些传感器与传统的空速传感器和攻角(AoA)叶片一起进行了表征,并显示了一次压力测量即可对AoA失速产生线性响应。飞行测试最初是通过俯仰操作手动驾驶飞机,然后使用来自单个压力端口的AoA估算值,基于闭环反馈进行一系列自动操作。 AoA估计值已成功用于控制飞机的姿态。训练了一个人工神经网络(ANN),以使用阵列中的所有压力端口估算AoA和空速,并使用试飞数据进行了验证。人工神经网络通过单端口方法更准确地估计了AoA,具有良好的鲁棒性以防止失速和不稳定流量。分布式流量传感器可用于补充传统的飞行控制系统,在机翼高度灵活或变形的系统中提供机翼流动状况的增强信息。

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