Estimation of Aerodynamics Forces in Dynamic Morphing Wing Flight

2025-08-04

AI Summary

To address the challenge of online accurate aerodynamic force estimation for dynamically morphing flapping-wing robots, this paper proposes a physics-informed, data-driven estimation method that requires no pre-collected training data. A reduced-order dynamical model is derived from Hamiltonian mechanics, and a conjugate momentum observer is designed to reconstruct generalized momentum in real time. This observer is coupled with a multilayer perceptron (MLP) regression network to enable high-frequency online estimation of the three-dimensional aerodynamic forces (Fₓ, Fᵧ, F_z). Experimental validation using a six-axis force sensor demonstrates excellent agreement between the proposed method and ground-truth measurements (RMSE < 0.03 N), significantly improving estimation accuracy and robustness. The approach integrates physical interpretability with data adaptivity without relying on prior training datasets. It establishes a novel paradigm for closed-loop control, high-fidelity modeling, and optimization design of morphing-wing flapping-wing robots.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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).

Problem

• Estimating aerodynamic forces in dynamic morphing wing flight
• Comparing physics-based and neural network force estimation methods
• Advancing control and design of bio-inspired flapping-wing robots

Innovation

• Physics-based observer using Hamiltonian mechanics
• Neural network regression model for force mapping
• High-frequency load cell for fine-grained measurements