Propeller thrust and drag in forward flight

Gill, Rajan; D’Andrea, Raffaello

doi:10.1109/ccta.2017.8062443

Cited by 64 publications

(50 citation statements)

References 15 publications

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“…In the context of control of multirotor vehicles, the typical modelling approach has been to use a hover or 'static' model, that is, the thrust and rotor torque are proportional to the square of the propeller rotation rate, and to neglect H-force, pitching and rolling moments [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. As shown in [15], this model breaks down when a quadrotor flies at moderate speeds. Thus in such cases, feedback control is relied upon to achieve adequate tracking.…”

Section: Literature Reviewmentioning

confidence: 99%

“…• Sections 2 and 3: a first-principles-based analytical model is derived for (1). This model is parametrized by nine parameters, which can either be optimized over using labelled data generated from flight experiments as in [13][14][15], or against wind tunnel data as will be done herein. In contrast to the literature, this section provides a consolidated and analytical grey-box model for (1) without limiting restrictions on β.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

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

Gill

D’Andrea

2019

Drones

Self Cite

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“…The thrust is generally modeled as T = k b ω 2 , which is called the thrust model at hover. This model, however, does not accurately predict the thrust and motor speed relation during high speed forward flights as it is observed in [26,27]. It was also concluded in [28] that, for the same rotor speed, the thrust decreases greatly at higher angles of attack and higher speeds.…”

Section: Thrust Irregularity In Forward Flightmentioning

confidence: 89%

UAV Payload Transportation via RTDP Based Optimized Velocity Profiles

Mohiuddin

Taha²,

Zweiri

et al. 2019

Energies

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This paper explores the application of a real-time dynamic programming (RTDP) algorithm to transport a payload using a multi-rotor unmanned aerial vehicle (UAV) in order to optimize journey time and energy consumption. The RTDP algorithm is developed by discretizing the journey into distance interval horizons and applying the RTDP sweep to the current horizon to get the optimal velocity decision. RTDP sweep requires the current state of the UAV to generate the next best velocity decision. To the best of the authors knowledge, this is the first time that such real-time optimization algorithm is applied to multi-rotor based transportation. The algorithm was first tested in simulations and then experiments were performed. The results show the effectiveness and applicability of the proposed algorithm.

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“…In literature, it is often assumed that the dominant contribution to aerodynamic drag forces is the aerodynamic interaction between the air and the propellers, see for example [14]. A derivation of the aerodynamic forces and torques using blade element theory for a propeller can be found, for example, in [15]. These models were shown to be a reasonable approximation to the true drag forces in certain flight regimes.…”

Section: Aerodynamic Forcesmentioning

confidence: 99%

State Estimate Recovery for Autonomous Quadcopters

Beffa

Ledergerber

D’Andrea

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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A method for recovery from the complete loss of the state estimate is presented for autonomous quadcopters. Given an aerodynamic force model, the only measurements used to reinitialize the state estimate by means of a bank of extended Kalman filters are the angular rate and linear acceleration measurements of an IMU. The method is integrated within a complete recovery logic on a quadcopter platform and experimentally evaluated. I. INTRODUCTION Over the course of the last two decades, unmanned aerial vehicles (UAVs), and particularly quadcopters, have received increasing attention. Low-cost, lightweight sensing technology paired with powerful processors enabled their widespread use as an experimental platform for research in the field of robotics, as well as in industry: for example for aerial delivery, inspection, surveillance, photography, mapping, inventory management and entertainment. With the emergent use of quadcopters, their safety and robustness have become important requirements. Ideally, a quadcopter is completely fault-tolerant, meaning that it is engineered in such a way that a failure affecting any of the components of the system does not prevent the intended operation being completed. An intuitive concept to create a fault-tolerant system is redundancy. By duplicating every component, a quadcopter can continue operation relying on the backup components, which almost completely eliminates the risk of a catastrophic failure. The major drawbacks of redundancy, however, are the increased cost, complexity and weight of the system. Redundancy for actuator fault-tolerance can alternatively be achieved by duplicating only a subset of components: For example, in [1], [2], [3] and [4] fault-tolerant control for hexacopters is studied. Another alternative way to achieve actuator redundancy is to introduce different means of actuation, for example by rotors that can be actively tilted [5]. In the context of sensor fault-tolerance, redundancy is typically not achieved by duplication of components, but by a sensor configuration that provides redundant information, see for example [6]. A summary of work on sensor and actuator fault-tolerance for quadcopters can be found in [7]. A less strict requirement is a fail-safe design: Such a system is designed to respond to a failure by taking an action that minimizes damage and/or harm to the equipment and its environment. For example, in [8] a control strategy is proposed that allows a quadcopter to continue controlled flight despite the loss of up to three propellers, without

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Propeller thrust and drag in forward flight

Cited by 64 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

UAV Payload Transportation via RTDP Based Optimized Velocity Profiles

State Estimate Recovery for Autonomous Quadcopters

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