无人机故障注入的硬件在环仿真

S. Gong, Shengwei Meng, Benkuan Wang, Datong Liu
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引用次数: 5

摘要

无人驾驶飞行器(uav)广泛应用于军事和民用领域。无人机的安全性越来越受到人们的重视。无人机的预测与健康管理(PHM)可以实现飞行过程中的故障预测,并根据潜在的故障做出相应的响应。从而提高了无人机的安全性和可靠性。然而,现有的无人机PHM研究存在以下两个挑战:a)历史飞行数据中的故障样本数量较少,无法覆盖无人机的多种故障模式以满足建模和验证的需求;b)无人机PHM技术无法通过实际飞行试验直接进行实际飞行验证。针对上述问题,本文提出了一种基于硬件在环仿真技术的故障数据生成方法。通过分析无人机飞控系统的故障特征,建立了无人机模型及其相应的故障模型。它可以模拟实际故障并生成多种故障模式的飞行数据,从而为PHM研究提供可用数据。此外,在机载PHM模块上实现的PHM算法可以通过该平台进行实时验证。实验结果表明,HILS平台具有良好的故障注入性能。
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Hardware-In-the-Loop Simulation of UAV for Fault Injection
Unmanned aerial vehicles (UAVs) are widely used in military and civilian applications. The safety of UAV has been paid more and more attention. Prognostic and Health Management (PHM) of UAV can realize the fault prediction during flight and make an appropriate response according to potential faults. Therefore, the safety and reliability of UAV are improved. However, the existing PHM research on UAVs has the following two challenges: a) the number of fault samples in historical flight data is small, and it is impossible to cover multiple fault modes of UAVs to meet the demand of modeling and verification, b) the actual flight verification of the UAV PHM technology cannot be carried out directly through actual flight test. Aiming at the above problems, this paper proposes a fault data generation method based on the Hardware-In-the-Loop Simulation (HILS) technology. We construct a UAV model and its corresponding fault models by analyzing fault features of UAV flight control system. It can simulate the actual faults and generate flight data with multiple fault modes, thereby available data are provided to support PHM research. Besides, PHM algorithms implemented on airborne PHM modules can be verified via this platform in real-time. The experiment results show that the HILS platform has a good performance for fault injection.
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