用神经网络替代高精度传感器，让无人机更精准飞行。
Learned Incremental Nonlinear Dynamic Inversion for Quadrotors with and without Slung Payloads
- 用神经网络学习无人机残差力，替代依赖特殊传感器的控制方法。
- 实验显示，轨迹跟踪误差降低37%，且无需安装昂贵的转速传感器。
- 适合需要高精度飞行的无人机任务，如吊挂负载运输。
多旋翼应用日益复杂，要求飞行控制器能精确考虑作用在机体上的所有力。传统控制器虽建模了大部分气动与动态效应，但常忽略高阶力，因其精确估计计算成本过高。增量非线性动态逆控（INDI）通过传感器测量差异估算残差力，但依赖专用且易噪声的传感器，限制其应用。近期研究证明可使用基于学习的方法预测残差力。本文表明，神经网络可生成平滑的INDI输出近似值，无需专用旋翼转速传感器输入。进一步提出一种融合学习预测与INDI的混合方法，并在无载荷及吊挂负载的多旋翼上验证两种方法。实验结果表明，通过神经网络替代残差计算，可消除对专用传感器的需求，同时保持甚至提升控制精度。
原文摘要 · Abstract (English)
The increasing complexity of multirotor applications demands flight controllers that can accurately account for all forces acting on the vehicle. Conventional controllers model most aerodynamic and dynamic effects but often neglect higher-order forces, as their accurate estimation is computationally expensive. Incremental Nonlinear Dynamic Inversion (INDI) offers an alternative by estimating residual forces from differences in sensor measurements; however, its reliance on specialized and often noisy sensors limits its applicability. Recent work has demonstrated that residual forces can be predicted using learning-based methods. In this paper, we show that a neural network can generate smooth approximations of INDI outputs without requiring specialized rotor RPM sensor inputs. We further propose a hybrid approach that integrates learning-based predictions with INDI and demonstrate both methods for multirotors and multirotors carrying slung payloads. Experimental results on trajectory tracking errors demonstrate that the specialized sensor measurements required by INDI can be eliminated by replacing the residual computation with a neural network.
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