. Wind tunnel testing of large deformable soft kites for wind energy conversion
is expensive and in many cases practically not feasible. Computational
simulation of the coupled fluid–structure interaction problem is
scientifically challenging and of limited practical use for aerodynamic
characterization. In this paper we present a novel experimental method for
aerodynamic characterization of flexible membrane kites by in situ
measurement of the relative flow, while performing complex flight maneuvers.
We find that the measured aerodynamic coefficients agree well with the values
that are currently used for flight simulation of soft kites. For flight
operation in crosswind maneuvers during which the traction force is kept constant,
the angle of attack is inversely related to the relative flow velocity. For
entire pumping cycles, the measurements show considerable variations in the
aerodynamic coefficients, while the angle of attack of the kite varies
only in a narrow range. This finding questions the commonly used
representation of aerodynamic coefficients as sole functions of the angle of
attack and stresses the importance of aeroelastic deformation for this type
of wing. Considering the effect of the power setting (identical to the trim)
solely as a rigid-body pitch rotation does not adequately describe the
aero-structural behavior of the kite. We show that the aerodynamic
coefficients vary as functions of the power setting (trim) of the kite, the
steering commands and the flight direction.

/pdf/aerodynamic-characterization-of-a-soft-kite-by-in-situ-flow-1nt51ibl2e.pdf

Aerodynamic characterization of a soft kite by in situ flow measurement

Fixed-interval smoothing, as one of the most important types of state estimation, has been concerned in many practical problems especially in the analysis of flight test data. However, the existing sequential filters and smoothers usually cannot deal with nonlinear or high-dimensional systems well. A state-of-the-art technique is employed in this study to explore the fixed-interval smoothing problem of a conceptual two-dimensional airfoil model in incompressible flow from noisy measurement data. Therein, the governing equations of the airfoil model are assumed to be known or only partially known. A single objective optimization problem is constructed with the classical Runge–Kutta scheme, and then estimations of the system states, the measurement noise and even the unknown parameters are obtained simultaneously through minimizing the objective function. Effectiveness and feasibility of the method are examined under several simulated measurement data corrupted by different measurement noises. All the obtained results indicate that the introduced algorithm is applicable for the airfoil model with cubic or free-play structural nonlinearity and leads to accurate state and parameter estimations. Besides, it is highly robust to Gaussian white and even more complex heavy-tailed measurement noises. It should be emphasized that the employed algorithm is still effective to high-dimensional nonlinear aeroelastic systems.

Fixed-interval smoothing of an aeroelastic airfoil model with cubic or free-play nonlinearity in incompressible flow

This paper presents a generic motion model to capture mobile robots' dynamic behaviors (translation and rotation). The model is based on statistical models driven by white random processes and is formulated into a full state estimation algorithm based on the error-state extended Kalman filtering framework (ESEKF). Major benefits of this method are its versatility, being applicable to different robotic systems without accurately modeling the robots' specific dynamics, and ability to estimate the robot's (angular) acceleration, jerk, or higher-order dynamic states with low delay. Mathematical analysis with numerical simulations are presented to show the properties of the statistical model-based estimation framework and to reveal its connection to existing low-pass filters. Furthermore, a new paradigm is developed for robots observability analysis by developing Lie derivatives and associated partial differentiation directly on manifolds. It is shown that this new paradigm is much simpler and more natural than existing methods based on quaternion parameterizations. It is also scalable to high dimensional systems. A novel \textbf{\textit{thin}} set concept is introduced to characterize the unobservable subset of the system states, providing the theoretical foundation to observability analysis of robotic systems operating on manifolds and in high dimension. Finally, extensive experiments including full state estimation and extrinsic calibration (both POS-IMU and IMU-IMU) on a quadrotor UAV, a handheld platform and a ground vehicle are conducted. Comparisons with existing methods show that the proposed method can effectively estimate all extrinsic parameters, the robot's translation/angular acceleration and other state variables (e.g., position, velocity, attitude) of high accuracy and low delay.

Robots State Estimation and Observability Analysis Based on Statistical Motion Models.

The present thesis is part of the beyond the sea® project which aims to develop tethered kite systems as auxiliary devices for ship propulsion. As a kite is a flexible structure, fluid-structure interaction has to be taken into account to calculate the flying shape and aerodynamic performances of the wing. A 3D Non-Linear Lifting Line model has been developed to deal with non-straight kite wings, with dihedral and sweep angles variable along the span and take into account the non-linearity of the section lift coefficient. The model has been checked with 3D RANSE simulations over various geometries and produces satisfactory results for range of incidence and sideslip up to 15°, with typical relative differences of few percent for the overall lift. The local results are also correctly estimated, the model is able to predict the position of the minimum and maximum loading along the span, even for a wing in sideslip. Simultaneously, a structure model has been developed. The core idea of the Kite as a Beam model is to approximate a Leading Edge Inflatable kite by an assembly of beam elements, equivalent each to a part of the kite composed of a portion of the inflatable leading edge, two inflatable battens and the corresponding canopy. The Kite as a Beam model has been compared to a complete kite Finite Element model over elementary comparison cases. The results show the behaviour differences of the two models, for example the torsion stiffness is globally overestimated by the Kite as a Beam model. Eventually, the Kite as a Beam model coupled with the 3D Non-Linear Lifting Line model is compared to the complete finite element model coupled with the 3D Non-Linear Lifting Line model. The gain in computation time is really significant but the results show the necessity of model calibration if the Kite as a Beam model should be used to predict the results of the complete finite element model.

Modeling with consideration of the fluid-structure interaction of the behavior under load of a kite for auxiliary traction of ships

This paper presents a controller design process for an aircraft tracking problem when not all states are available. In the study, a nonlinear-transport aircraft simulation model was used and identified through Maximum Likelihood Principle and Extended Kalman Filter. The obtained mathematical model was used to design a Linear–Quadratic Regulator (LQR) with optimal weighting matrices when not all states are measured. The nonlinear aircraft simulation model with LQR controller tracking abilities were analyzed for multiple experiments with various noise levels. It was shown that the designed controller is robust and allows for accurate trajectory tracking. It was found that, in ideal atmospheric conditions, the tracking errors are small, even for unmeasured variables. In wind presence, the tracking errors were proportional to the wind velocity and acceptable for small and moderate disturbances. When turbulence was present in the experiment, state variable oscillations occurred that were proportional to the turbulence intensity and acceptable for small and moderate disturbances.

System Identification and LQR Controller Design with Incomplete State Observation for Aircraft Trajectory Tracking

A flight-path reconstruction algorithm for tethered aircraft, which is based on an extended Kalman filter, is presented. The algorithm is fed by the measurements of a set of onboard and ground-base...

/pdf/flight-path-reconstruction-and-flight-test-of-four-line-4yesxqzq7f.pdf

A. Serino

Papers

Flight-Path Reconstruction and Flight Test of Four-Line Power Kites