Shear Wind Estimation

Bencatel, Ricardo; Girard, Anouck; Abdelhafiz, Mariam; Sousa, João Borges de

doi:10.2514/6.2011-6224

Cited by 11 publications

(7 citation statements)

References 10 publications

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“…The principle of estimation of wind field is to compare the differences between the value of state's estimation from dynamic equations and observations collected during the flight to estimate the parameters of wind shear by a kind of filter, such as Kalman filter, 47 extended Kalman filter, 55 particle filter, 48 Gaussian process regression, 45 and others. The adopted model, estimated accuracy, the drawbacks, and merits of some typical filters are listed in Table 1.…”

Section: Models Of Wind Shearmentioning

confidence: 99%

“…48 Wind shear is the necessary condition for dynamic soaring and has a significant impact on the performance of dynamic soaring, so it is crucial to find an accurate model to describe the wind profile. 49 In order to describe the wind shear above the sea surface and at ridges, the exponential model is adopted by Sachs et al, 17,31 Firtin et al, 50 Shen et al, 51 and Bower, 52 since the wind speed increases with a narrow layer before reaching the value of the free air stream.…”

Section: Models Of Wind Shearmentioning

confidence: 99%

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Energy extraction from wind shear: Reviews of dynamic soaring

Gao

Hou

Guo

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper presents a review of research works related to the application of dynamic soaring for extracting and collecting energy from certain structures in the wind using a small unmanned aerial vehicles (UAVs). By dynamic soaring, UAVs can extract energy from wind shear to produce electrical power or conduct a high-speed, long-endurance mission without adding extra weight. Combined with rapid technological development of UAVs, dynamic soaring might become a new way to explore nature's energy; extracted energy from wind shear might become a new source of renewable and sustainable energy. In this paper, we review the dynamic soaring and analyze the energy extraction process of it from the view of energy transformation. We also discuss the rate and efficiency of energy extraction as well as the possible applications, challenges and future of dynamic soaring in UAVs.

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Section: Models Of Wind Shearmentioning

confidence: 99%

Section: Models Of Wind Shearmentioning

confidence: 99%

Energy extraction from wind shear: Reviews of dynamic soaring

Gao

Hou

Guo

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…Consider a flowfield of the form (2) that is spatially uniform with vertical shear given by (3). The model (19) with climb-rate control…”

Section: A Parallel Formation Control With Speed Regulationmentioning

confidence: 99%

“…Corollary 2. The particle model (19) in flowfield (2) with climb rate control w k given by (13) with…”

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confidence: 99%

Dynamic Altitude Control for Motion Coordination in an Estimated Shear Flow

DeVries

Paley

2012

AIAA Guidance, Navigation, and Control Conference

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Windfields present a challenge to multi-vehicle coordination in applications such as environmental sampling. The presence of a windfield can disrupt inter-vehicle spacing such that the group covers less area, expends energy at a faster rate, strays from a desired formation, or provides irregular measurement data. However, an autonomous or remotely piloted vehicle can also take advantage of the vertical variation of a windfield to maintain a desired inertial speed and to coordinate its motion with other vehicles. This paper presents results for motion coordination in an estimated flowfield with wind shear using a three-dimensional self-propelled particle model with a dynamic altitude control. The paper derives conditions specifying the feasibility of speed-regulated trajectories and Lyapunov-based decentralized control algorithms to stabilize parallel formations in a known, uniform shear flow and symmetric circular formations in a known vortex flow.These algorithms are extended to unknown flowfields by presenting a distributed, recursive Bayesian filter that provides local estimates of the flow from noisy flowspeed measurements, allowing particles to utilize flowfield estimates in the control. Theoretical results are illustrated using numerical simulations. Nomenclature NNumber of particles in the system k Particle index; 1, . . . , N

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“…Thermal centering control was recently flight tested and reported in [15]. Various algorithms, such as Kalman filtering [16] or particle filtering [17] are utilized for the wind speed estimation. Other research on UAV energy extraction also combines a horizontal wind field with updrafts [18], [19].…”

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confidence: 99%

UAV energy extraction with incomplete atmospheric data using MPC

Liu¹,

Schijndel

Longo

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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Abstract-A Nonlinear Model Predictive Control strategy is presented for UAV trajectory determination. The objective is to find optimal paths in the atmosphere by maximizing the UAV's energy (kinetic and potential) over a finite but receding horizon. The main assumption is that the updraft distribution is unknown, creating a realistic situation. The updrafts are only estimated online using standard on-board inertial sensors. Realtime implementation of the algorithm is shown to be possible in principle.

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Shear Wind Estimation

Cited by 11 publications

References 10 publications

Energy extraction from wind shear: Reviews of dynamic soaring

Energy extraction from wind shear: Reviews of dynamic soaring

Dynamic Altitude Control for Motion Coordination in an Estimated Shear Flow

UAV energy extraction with incomplete atmospheric data using MPC

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