Aerodynamic analysis of a logistics UAV wing with compound ducted rotor

Guan, Yingjie; Li, Yongping; Zeng, Peng

doi:10.1108/aeat-03-2022-0074

Cited by 2 publications

(2 citation statements)

References 56 publications

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Section: Estimation Of Maximum Take-off Weightmentioning

confidence: 99%

“…is 4 kg, avionics equipment (including battery, flight controllers, electronic controls, GPS, etc.) is about 11 kg, radar life detector is 11 kg (including battery mass) (Guo et al , 2020; Yan et al , 2021; Guan et al , 2022) and the maximum take-off mass is about 30 kg: where W e is the empty weight of the aircraft, usually including the weight of the airframe structure and the weight of the power plant, W pows is the battery weight and W pl is the payload weight.…”

Section: Propeller Designmentioning

confidence: 99%

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Design and aerodynamic characteristics analysis of propellers for a novel rescue unmanned aerial vehicle

Guan,

2023

AEAT

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Purpose To solve the shortcomings of existed search and rescue drones, search and rescue the trapped people trapped in earthquake ruins, underwater and avalanches quickly and accurately, this paper aims to propose a four-axis eight-rotor rescue unmanned aerial vehicle (UAV) which can carry a radar life detector. As the design of propeller is the key to the design of UAV, this paper mainly designs the propeller of the UAV at the present stage. Design/methodology/approach Based on the actual working conditions of UAVs, this paper preliminarily estimated the load of UAVs and the diameters of propellers and designed the main parameters of propellers according to the leaf element theory and momentum theory. Based on the low Reynolds number airfoil, this paper selected the airfoil with high lift drag ratio from the commonly used low Reynolds number airfoils. The chord length and twist angle of propeller blades were calculated according to the Wilson method and the maximum wind energy utilization coefficient and were optimized by the Asymptotic exponential function. The aerodynamic characteristics of the designed single propeller and coaxial propeller under different installation pitch angles and different installation distances were analyzed. Findings The results showed that the design of coaxial twin propellers can increase the load capacity by about 1.5 times without increasing the propeller diameter. When the installation distance between the two propellers was 8 cm and the tilt angle was 15° counterclockwise, the aerodynamic characteristics of the coaxial propeller were optimal. Originality/value The novelty of this work came from the conceptual design of the new rescue UAV and its numerical optimization using the Wilson method combined with the maximum wind energy utilization factor and the exponential function. The aerodynamic characteristics of the common shaft propeller were analyzed under different mounting angles and different mounting distances.

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Section: Estimation Of Maximum Take-off Weightmentioning

confidence: 99%

Section: Propeller Designmentioning

confidence: 99%

Design and aerodynamic characteristics analysis of propellers for a novel rescue unmanned aerial vehicle

Guan,

2023

AEAT

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Automatic Landing Control for Fixed-Wing UAV in Longitudinal Channel Based on Deep Reinforcement Learning

Li,

Xu,

et al. 2024

Drones

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The objective is to address the control problem associated with the landing process of unmanned aerial vehicles (UAVs), with a particular focus on fixed-wing UAVs. The Proportional–Integral–Derivative (PID) controller is a widely used control method, which requires the tuning of its parameters to account for the specific characteristics of the landing environment and the potential for external disturbances. In contrast, neural networks can be modeled to operate under given inputs, allowing for a more precise control strategy. In light of these considerations, a control system based on reinforcement learning is put forth, which is integrated with the conventional PID guidance law to facilitate the autonomous landing of fixed-wing UAVs and the automated tuning of PID parameters through the use of a Deep Q-learning Network (DQN). A traditional PID control system is constructed based on a fixed-wing UAV dynamics model, with the flight state being discretized. The landing problem is transformed into a Markov Decision Process (MDP), and the reward function is designed in accordance with the landing conditions and the UAV’s attitude, respectively. The state vectors are fed into the neural network framework, and the optimized PID parameters are output by the reinforcement learning algorithm. The optimal policy is obtained through the training of the network, which enables the automatic adjustment of parameters and the optimization of the traditional PID control system. Furthermore, the efficacy of the control algorithms in actual scenarios is validated through the simulation of UAV state vector perturbations and ideal gliding curves. The results demonstrate that the controller modified by the DQN network exhibits a markedly superior convergence effect and maneuverability compared to the unmodified traditional controller.

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Aerodynamic analysis of a logistics UAV wing with compound ducted rotor

Cited by 2 publications

References 56 publications

Design and aerodynamic characteristics analysis of propellers for a novel rescue unmanned aerial vehicle

Design and aerodynamic characteristics analysis of propellers for a novel rescue unmanned aerial vehicle

Automatic Landing Control for Fixed-Wing UAV in Longitudinal Channel Based on Deep Reinforcement Learning

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