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Robust Fault Diagnosis for Quadrotor UAVs Using Adaptive Thau Observer

机译:基于自适应Thau观测器的四旋翼无人机的鲁棒故障诊断

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摘要

A robust Fault Diagnosis (FD) scheme for a real quadrotor Unmanned Aerial Vehicle (UAV) is proposed in this paper. Firstly, a novel Adaptive Thau observer (ATO) is developed to estimate the quadrotor system states and build a set of offset residuals to indicate actuators' faults. Based on these residuals, some rules of Fault Diagnosis (FD) are designed to detect and isolate the faults as well as estimate the fault offset parameters. Secondly, a synthetic robust optimization scheme is presented to improve Fault Estimation (FE) accuracies, three key issues include modeling uncertainties, and magnitude order unbalances as well as noises are addressed. Finally, a typical fault of rotors is simulated and injected into one of four rotors of the quadrotor, and experiments for the FD scheme have been carried out. Unlike former research works on the FD schemes for quadrotors, our proposed FD scheme based on the ATO can not only detect and isolate the failed actuators, but also estimate the fault severities. Regardless of roughness of the real flying data, the FD results still have sufficient FE accuracies.
机译:提出了一种针对四旋翼无人机的鲁棒故障诊断(FD)方案。首先,开发了一种新型的自适应Thau观测器(ATO)来估计四旋翼系统的状态并建立一组偏移残差以指示执行器的故障。基于这些残差,设计了一些故障诊断(FD)规则,以检测和隔离故障以及估计故障偏移参数。其次,提出了一种综合的鲁棒优化方案,以提高故障估计(FE)的准确性,这三个关键问题包括建模不确定性,并解决了量级不平衡以及噪声。最后,对典型的转子故障进行了仿真,并将其注入四旋翼的四个转子之一,并进行了FD方案的实验。与以前对四转子FD方案的研究工作不同,我们基于ATO提出的FD方案不仅可以检测和隔离故障执行器,而且可以估计故障严重程度。不管实际飞行数据的粗糙度如何,FD结果仍然具有足够的FE精度。

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