用临界慢化原理提前0.9秒预测四轴飞行器失控，无需模型和失控数据。
Critical slowing down for predicting controller induced loss of control in quadrotors
- 基于临界慢化现象，不依赖系统模型预测失控。
- 提前0.9秒预警失控，准确率高于现有神经网络方法。
- 无需重新参数化，可跨机型、场景泛化预测失控。
我们提出一种新型预测方法，用于提前预警四轴飞行器因控制器不稳定导致的失控（LOC），并在来自四种不同四轴飞行器的真实失控飞行数据上进行了评估。该方法利用临界慢化（CSD）这一普遍存在于复杂生态与生物系统中关键转变前的早期信号，其优势在于无需系统模型即可实现预测。实验基于实际飞行数据，其中失控由输入输出延迟引发。所提方法可提前最多0.9秒预测失控，检测准确率优于当前最先进的循环神经网络预测器，且对失控事件的数据依赖更少。特别地，我们仅利用临界慢化机制，在未使用失控事件自身数据的情况下实现了精准预测。进一步地，该方法无需重新参数化，成功应用于其他四轴飞行器在室内外飞行路径中的不同失控场景，表明其能有效泛化至不同控制器架构、飞行器及失控情境。
原文摘要 · Abstract (English)
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.
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