Estimation of Aerodynamics Forces in Dynamic Morphing Wing Flight

TL;DR

This paper compares a physics-based observer and a neural network model for estimating aerodynamic forces in a bio-inspired flapping-wing robot, enabling better control during tethered flight.

Contribution

It introduces and evaluates two distinct force estimation methods—Hamiltonian mechanics-based observer and neural network regression—for dynamic morphing wing flight.

Findings

Both estimators agree well on force components Fx, Fy, Fz.

The methods enable real-time force estimation during wingbeats.

The approaches improve understanding of aerodynamic contributions in bio-inspired flight.

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).