通过物理观测与神经网络，实时估算扑翼机器人的气动力，助力飞行控制。
Estimation of Aerodynamics Forces in Dynamic Morphing Wing Flight
- 基于哈密顿力学的共轭动量观测器，无需训练数据实时估计力。
- 用多层感知机学习关节运动与气动力关系，三方向力估计吻合度高。
- 适合做仿生扑翼飞行器控制与建模的研究者参考。
精确估计气动力对提升具有动态变形能力的扑翼空中机器人在控制、建模和设计方面至关重要。本文研究了两种方法在Aerobat（一种模仿蝙蝠飞行惯性与气动特性的仿生扑翼平台）上的应用，旨在量化悬停飞行中气动力贡献，为闭环飞行控制奠定基础。第一种方法是基于哈密顿力学的物理观测器，利用共轭动量推断外部气动力；该方法基于系统简化动力学模型，结合实时传感器数据，无需训练数据即可实现力估计。第二种方法采用多层感知机（MLP）神经网络回归模型，学习从关节运动、拍打频率和环境参数到气动力输出的映射关系。我们在高频数据采集系统中使用六轴力传感器评估两种估计算法，结果表明两者在三个力分量（Fx, Fy, Fz）上表现出高度一致性。
原文摘要 · Abstract (English)
Accurate estimation of aerodynamic forces is essential for advancing the control, modeling, and design of flapping-wing aerial robots with dynamic morphing capabilities. In this paper, we investigate two distinct methodologies for force estimation on Aerobat, a bio-inspired flapping-wing platform designed to emulate the inertial and aerodynamic behaviors observed in bat flight. Our goal is to quantify aerodynamic force contributions during tethered flight, a crucial step toward closed-loop flight control. The first method is a physics-based observer derived from Hamiltonian mechanics that leverages the concept of conjugate momentum to infer external aerodynamic forces acting on the robot. This observer builds on the system's reduced-order dynamic model and utilizes real-time sensor data to estimate forces without requiring training data. The second method employs a neural network-based regression model, specifically a multi-layer perceptron (MLP), to learn a mapping from joint kinematics, flapping frequency, and environmental parameters to aerodynamic force outputs. We evaluate both estimators using a 6-axis load cell in a high-frequency data acquisition setup that enables fine-grained force measurements during periodic wingbeats. The conjugate momentum observer and the regression model demonstrate strong agreement across three force components (Fx, Fy, Fz).
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