Experimental Characterization of a Propulsion System for Multi-rotor UAVs

Sartori, Daniele; Wang, Yu

doi:10.1007/s10846-019-00995-2

Cited by 17 publications

(10 citation statements)

References 19 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%

“…• 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

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“…The principle behind in-flight propeller modelling and identification was already demonstrated by Sartori [1] who identified thrust, drag and side force on propellers from mostly planar indoor flights. Earlier also Gill [9] performed propeller identification based on a tethered experiment with a multirotor allowing it to reach higher speeds indoor.…”

Section: Introductionmentioning

confidence: 93%

“…Mechanically, a multirotor can be split into a fixed structure or 'body' and the rotating propellers [1]. Ignoring the aerodynamic effects of the propeller slipstreams, interaction between body and propellers consists of three forces and moments and rotational speed of the motor shaft to the propeller hub as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Virtual Motor Torque Sensing for Multirotor Propulsion Systems

Theys

Schutter

2021

IEEE Robot. Autom. Lett.

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This paper presents a method to sense propeller torque of multirotors without additional torque sensors by using the measured input voltage, throttle setting and rotational speed. Torque, rotational speed and current of a brushless direct current motor with electronic speed controller are measured on a setup using seven different propeller diameters, seven input voltages and 20 throttle settings. The resulting rotational speed, torque and current are measured to create a data set spanning the feasible operating range of this motor with speed controller. A model based on only four parameters is proposed and trained on the data of only one propeller and the data without propeller. This model results in torque errors less than 0.01 N m for 90% of the data set. For the highest accuracy, the whole data set is re-mapped into a practical 3D lookup table using local fits and results in torque errors less than 0.005 N m for 90% of the data set.

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“…For the aileron-less control scheme, an accurate engine system model is helpful for the decrease of control input, and the engine system of the UAV is composed of a motor, a propeller, and an electronic speed regulator (ESC). In this paper, the engine model is obtained based on the static and dynamic experiments [14], and the combination of DualSky-2814 brush-less motor and a 10-inch diameter, 7-inch pitch propeller is chosen according to the thrust requirement and efficiency on the standard operation condition. In the static experiment, the motor and propeller are mounted on an ATI Gamma 6-DOF force/torque sensor and then connected to a fixed clamp on the desk, and the static thrust and torque values are collected by the data acquisition hardware and software system.…”

Section: Engine Systemmentioning

confidence: 99%

Automatic Control and Model Verification for a Small Aileron-Less Hand-Launched Solar-Powered Unmanned Aerial Vehicle

et al. 2020

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This paper describes a low-cost flight control system of a small aileron-less hand-launched solar-powered unmanned aerial vehicle (UAV). In order to improve the accuracy of the whole system model and quantify the influence of each subsystem, detailed modeling of UAV energy and a control system including a solar model, engine, energy storage, sensors, state estimation, control law, and actuator module are established in accordance with the experiment and component principles. A whole system numerical simulation combined with the 6 degree-of-freedom (DOF) simulation model is constructed based on the typical mission route, and the parameter precision sequence and energy balance are obtained. Then, a hardware-in-the-loop (HIL) experiment scheme based on the Stewart platform (SP) is proposed, and three modes of acceleration, angular velocity, and attitude are designed to verify the control system through the inner and boundary states of the flight envelope. The whole system scheme is verified by flight tests at different altitudes, and the aerodynamic force coefficient and sensor error are corrected by flight data. With the increase of altitude, the cruise power increases from 47 W to 78 W, the trajectory tracking precision increases from 23 m to 44 m, the sensor measurement noise increases, and the bias decreases.

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Experimental Characterization of a Propulsion System for Multi-rotor UAVs

Cited by 17 publications

References 19 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

Virtual Motor Torque Sensing for Multirotor Propulsion Systems

Automatic Control and Model Verification for a Small Aileron-Less Hand-Launched Solar-Powered Unmanned Aerial Vehicle

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