Simulation of the Transition Phase for an Optimally-Controlled Tethered VTOL Rigid Aircraft for AirborneWind Energy Generation

Rushdi, Mostafa A.; Hussein, Ahmed; Dief, Tarek N.; Yoshida, Sheigeo; Schmehl, Roland

doi:10.2514/6.2020-1243

Cited by 4 publications

(1 citation statement)

References 34 publications

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“…There are several papers that deal with model-based AWE systems for kites [13,[26][27][28] and tethered aircraft [29][30][31] as the flying devices of an AWE system, including a simulated approach for training constrained Gaussian processes models [32]; however, there is a lack of papers based on experimental measurements [33] using data-driven methods [34,35], although, system identification was used in several papers [28,[36][37][38]. In a predecessor paper [39], experimental data from early flight tests was presented, but without any data analysis.…”

Section: Machine Learning Methods In Awementioning

confidence: 99%

Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

Rushdi

Dief

et al. 2020

Energies

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Kites can be used to harvest wind energy at higher altitudes while using only a fraction of the material required for conventional wind turbines. In this work, we present the kite system of Kyushu University and demonstrate how experimental data can be used to train machine learning regression models. The system is designed for 7 kW traction power and comprises an inflatable wing with suspended kite control unit that is either tethered to a fixed ground anchor or to a towing vehicle to produce a controlled relative flow environment. A measurement unit was attached to the kite for data acquisition. To predict the generated tether force, we collected input–output samples from a set of well-designed experimental runs to act as our labeled training data in a supervised machine learning setting. We then identified a set of key input parameters which were found to be consistent with our sensitivity analysis using Pearson input–output correlation metrics. Finally, we designed and tested the accuracy of a neural network, among other multivariate regression models. The quality metrics of our models show great promise in accurately predicting the tether force for new input/feature combinations and potentially guide new designs for optimal power generation.

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Section: Machine Learning Methods In Awementioning

confidence: 99%

Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

Rushdi

Dief

et al. 2020

Energies

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Transition Nonlinear Blended Aerodynamic Modeling and Anti-Harmonic Disturbance Robust Control of Fixed-Wing Tiltrotor UAV

Liao

Bang

2023

Drones

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This study proposed a novel nonlinear blended aerodynamic model for the tiltrotor unmanned aerial vehicle (UAV) during the transition phase to handle the high angle-of-attack (AoA) flight, which aggregated the flat-plate mode and the linear mode of the aerodynamic coefficients. Additionally, a harmonic disturbance observer (HDO) and super-twisting sliding mode controller (STSMC) addressed the fast-changing external disturbances and attenuated the chattering problem in the original SMC. The comparative trajectory tracking results indicated that the blended aerodynamic model accurately tracks the reference signals with no tracking errors, which demonstrated a superior performance as compared to the traditional aerodynamic model, with a reduction of 2.2%, 50%, 73.6%, and 11.2% in the time required for tracking the pitch angle, pitch rate, and velocities u and w, respectively. Conversely, the traditional one exhibited significant tracking errors, ranging from 0.016° in the pitch angle channel to 1.25°/s in the pitch rate channel, and 0.6 m/s for velocity u and 0.01 m/s for velocity w. Moreover, the comparative control input results illustrated that the least control effort was required for the proposed HDO-STSMC control scheme with a blending function, while the original ESO-SMC experienced more oscillations and sharp amplitude changes, taking twice the time to converge, with considerable tracking errors such as 1.067° in the pitch angle channel, 0.788°/s in the pitch rate channel, 1.554 m/s for velocity u, and 0.746 m/s for velocity w, which verified the feasibility and superiority of the proposed HDO-STSMC with the blending function. Two performance indices revealed the robust stability and rapid convergence of the proposed transition blended aerodynamic model with the HDO-STSMC control scheme.

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A Review on Crosswind Airborne Wind Energy Systems: Key Factors for a Design Choice

Pereira

Sousa

2022

Energies

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Airborne wind energy (AWE) has received increasing attention during the last decade, with the goal of achieving electricity generation solutions that may be used as a complement or even an alternative to conventional wind turbines. Despite that several concepts have already been proposed and investigated by several companies and research institutions, no mature technology exists as yet. The mode of energy generation, the type of wing, the take-off and landing approaches, and the control mechanisms, to name a few, may vary among AWE crosswind systems. Given the diversity of possibilities, it is necessary to determine the most relevant factors that drive AWE exploration. This paper presents a review on the characteristics of currently existing AWE technological solutions, focusing on the hardware architecture of crosswind systems, with the purpose of providing the information required to identify and assess key factors to be considered in the choice of such systems. The identified factors are categorized into four distinct classes: technical design factors (aerodynamic performance, mass-to-area ratio, durability, survivability); operational factors (continuity of power production, controllability, take-off and landing feasibility); fabrication and logistical factors (manufacturability, logistics); and social acceptability factors (visual impact, noise impact, ecological impact, safety).

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Simulation of the Transition Phase for an Optimally-Controlled Tethered VTOL Rigid Aircraft for AirborneWind Energy Generation

Cited by 4 publications

References 34 publications

Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

Transition Nonlinear Blended Aerodynamic Modeling and Anti-Harmonic Disturbance Robust Control of Fixed-Wing Tiltrotor UAV

A Review on Crosswind Airborne Wind Energy Systems: Key Factors for a Design Choice

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