Real-Time Optimizing Control of an Experimental Crosswind Power Kite

Costello, Sean; François, Grégory; Bonvin, Dominique

doi:10.1109/tcst.2017.2672404

Cited by 15 publications

(9 citation statements)

References 42 publications

(71 reference statements)

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“…167ff.). Equations (25)(26)(27)(28)(29) imply the assumption that aerodynamic interferences, for example, between wings and rotors are negligible or can be modeled with appropriate values for e and C D;k;o . Finally, the airfoil lift and drag coefficients are related: Apart from stall, the drag coefficient of an airfoil (also called profile drag) increases approximately quadratically with the airfoil's lift coefficient.…”

Section: E Total Lift and Drag Coefficientsmentioning

confidence: 99%

“…and C L;n and C D;eq;n are the nominal total lift and equivalent drag coefficients that occur at c L c L;n and C D;k;a C D;k;a;min 0 in Eqs. (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). That optimal value v a;opt is used as the set value of the airflow speed v a;set , but bounded by the minimum and maximum values, that is, v a;set limitv a;min ; v a;opt ; v a;max (70)…”

Section: Tangential Speed Set Valuementioning

confidence: 99%

“…As from Eqs. (25), (22), (11), (28), (30), (27), (26), (24), (21), (12), (10), (3), and (78) follows F tan c 0 c 1 c L c 2 c 2 L with some values c 0 , c 1 , and c 2 , the set value is found (exactly) by quadratic interpolation. For that, three points are required.…”

Section: Tangential Force Control Allocation Algorithmmentioning

confidence: 99%

“…Fagiano et al [24] proposed a proportional controller on a control-oriented submodel to track the kite's flight trajectory, which was then used and extended by others (cf., e.g., [25]). Further studies target a maximum power point tracking-like approach to optimize the power generation (cf., e.g., [26,27]).…”

Section: Introductionmentioning

confidence: 99%

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Control of a Drag Power Kite over the Entire Wind Speed Range

Bauer

Petzold

Kennel

et al. 2019

Journal of Guidance, Control, and Dynamics

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A control scheme for drag power kites, also known as airborne wind turbines, for the entire wind speed range is proposed, including 1) a temperature controller allowing for temporary overloading of the powertrain; 2) a limitation controller ensuring that power, force, speed, and actuator constraints are satisfied; 3) a tangential flight speed controller; and 4) a tangential force control allocation, which inverts the nonlinearities of the plant and allocates the flight speed controller's tangential force demand to the available actuators. The drag power kite plant model is based on a point-mass model and a simple aerodynamics model with various drag contributions. Simulations are conducted with the parameters of the 20 kW Wing 7 developed by Makani Power, Inc. The proper working of the control scheme is indicated by the good match of the simulation results with independent simulation results and measurements published by Makani. A temporary overloading of the powertrain with about twice the nominal power can be concluded as a requirement; otherwise the mean power would be significantly lower. Because of the reduction of the lift and thus reduction of the centripetal force at high wind speeds, the inside-down figure eight can be concluded as the best pattern.

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Section: E Total Lift and Drag Coefficientsmentioning

confidence: 99%

Section: Tangential Speed Set Valuementioning

confidence: 99%

Section: Tangential Force Control Allocation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Control of a Drag Power Kite over the Entire Wind Speed Range

Bauer

Petzold

Kennel

et al. 2019

Journal of Guidance, Control, and Dynamics

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“…where E is the glide ratio, L is the line length which is assumed to be constant, v w the wind speed, K a constant steering gain and u the input. This kinematic model is similar to the models employed for guidance control design in [29], [4], [3].…”

Section: A Kite Modelmentioning

confidence: 99%

Stability Verification for Periodic Trajectories of Autonomous Kite Power Systems

Ahbe

Wood

Smith

2018

2018 European Control Conference (ECC)

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Model-based predictive controllers are frequently employed in Airborne Wind Energy (AWE) systems to follow reference flight paths. In this paper we compute the region of attraction (ROA) of path-stabilizing feedback gains around a closed trajectory as typically flown by a power generating kite. The feedback gains are obtained from applying a time-varying periodic Linear Quadratic Regulator to the system expressed in transversal coordinates. To compute the ROA we formulate Lyapunov stability conditions which we verify by employing semidefinite relaxations of the Positivstellensatz. This results in an optimization problem for which we compare various definitions of the cost function representing the size of the ROA. In a numeric case study we demonstrate that the maximization of an expanding ellipse inside of a quartic Lyapunov functions leads to significantly larger verified ROAs compare to the standard approach of optimizing over the scaling of the sublevel set of a quadratic Lyapunov function.

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Automatic testbed with a visual motion tracking system for airborne wind energy applications

et al. 2023

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The architecture and a flight test campaign of a small‐scale testbed aimed at aerodynamic and dynamic characterization of airborne wind energy systems are presented. The testbed involves a two‐line rigid‐framed delta kite and an automatic ground station for the lateral control of the kite and reel‐in/reel‐out of the two tethers. The environment, and the states of the kite, the tethers and the actuators are measured by a set of on‐ground and onboard sensors that include, among others, an inertial measurement unit, GNSS receivers, load cells, actuator encoders, a wind station, and a visual motion tracking (VMT) system based on three cameras and an artificial neural network (YOLOv2). The results of a 5‐min flight, including the take‐off, cross‐wind flight, and landing, were used to analyze the capabilities of the testbed. It was shown that the time derivative of the kite course angle exhibits a linear correlation with both the delayed steering input and the delayed differential tether tension, being the dispersion lower for the latter. The intrinsic and extrinsic calibrations proposed for the VMT system led to a good agreement between the estimation of the kite position and course angle provided by the VMT system and the onboard computer. Moreover, although the YOLOv2 algorithm failed in the detection of the kite within around 5% of the images, the simultaneous non‐detection from the three cameras was below 0.1% during the full flight. Such a reliability suggests that a VMT system can be used as a redundant or backup sensor for the GNSS.

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Real-Time Optimizing Control of an Experimental Crosswind Power Kite

Cited by 15 publications

References 42 publications

Control of a Drag Power Kite over the Entire Wind Speed Range

Control of a Drag Power Kite over the Entire Wind Speed Range

Stability Verification for Periodic Trajectories of Autonomous Kite Power Systems

Automatic testbed with a visual motion tracking system for airborne wind energy applications

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