用于预测四旋翼飞行器中控制器引发的失控的临界减速

中文摘要 AI 辅助

我们开发了一种新颖的预测方案来预测四旋翼飞行器中控制器引发的失控（LOC），并在来自四个不同四旋翼飞行器的真实LOC飞行数据上进行评估。为此，利用临界减速（CSD）得出LOC的早期预警信号，CSD是一种普遍现象，在各种复杂生态和生物系统的临界转变之前出现。因此，我们的早期预警指标具有通用性，无需系统模型即可预测LOC。该方法在真实四旋翼飞行数据上进行评估，其中LOC是由输入输出延迟导致的不稳定控制器行为引起的。我们的方法在LOC发生前可达0.9秒的时间预测，在检测精度和LOC数据依赖方面优于现有循环神经网络四旋翼LOC预测器。特别是，我们利用CSD的见解在不使用LOC事件本身数据的情况下准确预测LOC。此外，我们的预测器无需重新参数化即可应用于预测不同的LOC场景，即四旋翼飞行路线，该场景发生在室内和室外飞行的其他四旋翼飞行器上。尽管存在这些差异，我们的方法仍成功检测到LOC，表明它可以在控制器架构、四旋翼飞行器和LOC场景中通用。

英文摘要

We develop a novel forecasting scheme to anticipate controller induced loss of control (LOC) in quadrotors and evaluate it on real LOC flight data from four different quadrotors. For this, early warning signals of LOC are derived using critical slowing down (CSD), a generic phenomenon shown to precede critical transitions across various complex ecological and biological systems. As such, our early warning indicators are generic in the sense that no system models are needed to facilitate forecasts of LOC. The approach is evaluated on real quadrotor flight data wherein LOC occurs due to unstable controller behavior arising from input-output delays. Our approach achieves a time-to-LOC forecast of up to 0.9 seconds before LOC occurs, outperforming state-of-the-art recurrent neural network quadrotor LOC forecasters in terms of detection accuracy and LOC data reliance. In particular, we leverage insights from CSD to accurately predict LOC without using data of the LOC event itself. Going further, we apply our forecasters without any re-parameterization to anticipate a different LOC scenario, quadrotor flyways, that occur on other quadrotors flying both indoors and outdoors. Despite these differences, our approach successfully detects LOC, demonstrating that it can generalize across controller architectures, quadrotors, and LOC scenarios.

发表机构

• Delft University of Technology（代尔夫特理工大学）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

27 pages, 14 figures