发表机构
Helmut Schmidt University; University of the Bundeswehr Munich（赫尔穆特·施密特大学; 慕尼黑联邦国防军大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究将条件可逆神经网络用于多旋翼控制，通过有理二次样条耦合等方法学习概率逆动力学模型，开环再现效果良好，闭环场景中位置RMSE与INDI匹配，分析出主要故障机制。
AI 中文摘要
我们研究将条件可逆神经网络（cINNs）作为多旋翼控制的概率逆动力学模型。对于平面X8同轴多旋翼，我们使用有理二次样条耦合和可逆线性混合从增量非线性动态逆（INDI）教师那里学习$p(u \mid s_t, c_t)$。开环再现达到$R^2 = 0.944$，平均CRPS为0.0915，对数概率误差相关性$\rho = -0.60$。在15个闭环场景中，位置RMSE与INDI匹配（9.7对9.5米），47%的跟踪可接受；故障分为激进步骤下的姿态发散和高频参考下的相位滞后，确定命令带宽和数据覆盖为主要故障机制。
英文摘要
We investigate conditional invertible neural networks (cINNs) as probabilistic inverse-dynamics models for multirotor control. For a planar X8 coaxial multicopter, we learn $p(u \mid s_t, c_t)$ from an incremental nonlinear dynamic inversion (INDI) teacher using rational-quadratic spline coupling and invertible linear mixing. Open-loop reproduction reaches $R^2 = 0.944$, mean CRPS 0.0915, and log-probability-error correlation $ρ= -0.60$. Over 15 closed-loop scenarios, position RMSE matches INDI (9.7 vs. 9.5 m), with 47 percent tracking acceptably; failures separate into attitude divergence under aggressive steps and phase lag under high-frequency references, isolating command bandwidth and data coverage as dominant failure mechanisms.
