Influence of Forward and Descent Flight on Quadrotor Dynamics

Orsag, Matko; Bogdan, Stjepan

doi:10.5772/37438

Cited by 16 publications

(6 citation statements)

References 15 publications

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“…Recently, a few works have used blade element theory (BET) [5,6,14,15,[62][63][64][65][66], usually in conjunction with the Glauert flow model [34] for both axial and oblique flow conditions, resulting in models with no closed form expressions. Additionally, the models are fragmented in the literature, with some focusing only on the axial flow condition, and others only considering a subset of the outputs in (1).…”

Section: Literature Reviewmentioning

confidence: 99%

Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications

Gill

D’Andrea

2019

Drones

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Two low-order, parametric models are developed for the forces and moments that a rotating propeller undergoes in forward flight. The models are derived using a first-principles-based approach, and are computationally efficient in the sense of being represented by explicit expressions. The parameters for the models can be identified either using supervised learning/grey-box fitting from labelled data, or can be predicted using only the static load coefficients (i.e., the hover thrust and torque coefficients). The second model is a multinomial model that is derived by means of a Taylor series expansion of the first model, and can be viewed as a lower-order lumped parameter model. The models and parameter generation methods are experimentally tested against 19 propellers tested in a wind tunnel under oblique flow conditions, for which the data is made available. The models are tested against 181 additional propellers from existing datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications

Gill

D’Andrea

2019

Drones

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“…In this study, thrust force is assumed to be proportional to the square of angular speed of rotor. By means of measuring voltage, rpm, induced velocity, and thrust force, twist angle of the rotor is estimated [7].…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Modeling and Parameter Estimation of a Quadrotor Helicopter

Kaya

Kutay

2014

AIAA Atmospheric Flight Mechanics Conference

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This study focuses on aerodynamic modeling of a quadrotor helicopter and the estimation of the model parameters in wind tunnel tests for hover, vertical climb, and forward flight conditions. The motion of a quadrotor is mainly affected by the aerodynamic forces and moments generated by rotors. Accurate calculation of rotor loads is essential for high fidelity simulation of a quadrotor. Momentum and blade element theories are used to obtain expressions for rotor forces and moments for a traveling vehicle. The parameters of the models are then identified through wind tunnel tests where the forces and moments created under various wind conditions and rotor speeds are measured with a six axes balance system. Nomenclature= angular speed of the rotor = density of air = inflow angle = inflow ratio = rotor advanced ratio = solidity ratio , , = Euler angles = overall residual propeller angular speed ̅̅̅̅ = average drag coefficient ̅ = average of observed values ̂ = fitted value = induced velocity = side force coefficient = hub force coefficient = pitching moment coefficient = drag moment (rotor torque) coefficient = rolling moment coefficient = thrust force coefficient = aerodynamic body coefficient of the quadrotor in x, y, and z body axes, respectively = lift coefficient = moments of inertia = products of inertia = resultant inertia moment in the z direction = aerodynamic body moments in x, y,and z body axes, respectively = Error Sums of Squares = Regression Sum of Squares = Total Sum of Squares = vertical force of blade = horizontal force of blade = climb velocity = aerodynamic body forces in x, y, and z body axes, respectively ̇ ̇ ̇ = velocity of the rotor in x, y, and z body axes, respectively = angle of attack (a.o.a.) of the rotor 1 Research Assistant at CPC, Department of Aerospace Engineering, dekaya@metu.edu.tr, Student Member of AIAA 2 Assistant Prof., Department of Aerospace Engineering, kutay@metu.edu.tr, Senior Member of AIAA Downloaded by CORNELL UNIVERSITY on July 31, 2015 | http://arc.aiaa.org | 2 = twist angle = distance between rotor center and Center of Gravity (C.o.G.) n = number of test cases/observations = radius of the rotor R= rolling moment = free stream velocity = observed/measured value = side force = area captured by rotor = hub force = resultant aerodynamic moments acting on the quadrotor in x, y, z body axes, respectively = number of blades = pitching moment = drag moment (rotor torque) = surface area of the quadrotor = thrust force = resultant aerodynamic forces acting on the quadrotor in x, y, z body axes, respectively = lift curve slope = thrust force coefficient for hover = drag moment coefficient for hover = drag acting on the blade = lift acting on the blade = error term = gravitational acceleration = distance between rotor axis and Center of Gravity (C.o.G) = mass of the quadrotor = angular speeds of the quadrotor in x, y, and z body axes, respectively = linear speeds of the quadrotor in x, y, and z body axes, respectively = sideslip angle y = pitch angle = total vertical forces actin...

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“…The forces and torques acting on each infinitesimal portion of the blade are integrated over the whole propeller [24]. This theory has been adopted to model aerodynamic effects on a quadrotor in many studies [25][26][27][28][29]. However, both blade-element theory and momentum theory require the value of the induced velocity which is challenging to estimate.…”

Section: Related Workmentioning

confidence: 99%

NeuroBEM: Hybrid Aerodynamic Quadrotor Model

Bauersfeld

Kaufmann

Foehn

et al. 2021

Robotics: Science and Systems XVII

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Fig. 1: Long-exposure images depicting quadrotor trajectory tracking at speeds up to 65 km/h in a large-scale motion-capture system. The captured data is used to fit a hybrid quadrotor model combining blade-element-momentum (BEM) theory with a neural network compensating residual dynamics. This hybrid model reproduces the flown trajectories in simulation with a positional RMSE error reduction of over 50% compared to state-of-the-art.

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Influence of Forward and Descent Flight on Quadrotor Dynamics

Abstract: Hard cover, 544 pagesPublisher InTech

Cited by 16 publications

References 15 publications

Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications

Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications

Aerodynamic Modeling and Parameter Estimation of a Quadrotor Helicopter

NeuroBEM: Hybrid Aerodynamic Quadrotor Model

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