Strain state estimation of very flexible unmanned aerial vehicle

This paper presents two different methods for estimating wing shape and rigid body attitude using displacement (stereovision reconstruction) and orientation information (inertia measurement units) at discrete points along a very flexible wing. First, theoretical relationship between the system states (strain, strain rate, body velocity, attitude etc.) and the sensor measurements are derived. Using the derived relationship, a nonlinear least squares fit is developed to obtain wing shape snapshots as well as rigid body attitude. The second method employs a Kalman filter to obtain both shape and rate information. The first method is more accurate since it employs nonlinear strain-displacement relationship at the cost of higher computational cost. However, in the presence of noise in the sensor measurement, the Kalman filter is faster and performs better in terms of accuracy.