Direct Sensing of Thrust and Velocity for a Quadrotor Rotor Array

Davis, Edwin; Pounds, Paul E. I.

doi:10.1109/lra.2017.2668471

Cited by 24 publications

(13 citation statements)

References 7 publications

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“…Given also the slow dynamics of the vehicle compared to the filter, we make the following necessary assumptionβ a 1 = β a 1 .ā − β a 1 is as close as one can get to the true acceleration of the vehicle. This is what distinguishes our work from previous work and gives us better results than the state-of-the art [31], [32], [29], [26], [25], [44].…”

Section: Resultsmentioning

confidence: 72%

“…[26] used aerodynamic power [27] to estimate induced flow through the rotor, however, there is insufficient information with this approach to discriminate between motions in e 1 , e 2 and e 3 directions [28]. Davis and Pounds [29] used a force torque sensor to estimate total velocities of up to 1m/s with an accuracy of about 0.1m/s. A more promising approach is based on exploiting the linear induced drag effects of rotor motion [30] to estimate translational linear velocities.…”

Section: Monocular Visual Slamxmentioning

confidence: 99%

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Supervisory Control of Multirotor Vehicles in Challenging Conditions Using Inertial Measurements

et al. 2018

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Section: Resultsmentioning

confidence: 72%

Section: Monocular Visual Slamxmentioning

confidence: 99%

Supervisory Control of Multirotor Vehicles in Challenging Conditions Using Inertial Measurements

et al. 2018

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“…There are challenges in applying such automated approaches, however, due to the structure and components of the system, which comprises power lines, insulators and pylons of various structures. In addition, UAVs are restricted by the non-contact sensing technologies they can use [9]- [11]. Accordingly, there have been numerous research studies conducted to automate inspect power systems approaches; however, there is a paucity of comprehensive published reviews which highlight certain issues (i.e., maintenance, inspection) for different approaches to the problem [12]-This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Unmanned Aerial Vehicle (UAV) Approach via Learning-Based Control for Overhead Power Line Monitoring: A Comprehensive Review

Foudeh

Luk²,

Whidborne

2021

IEEE Access

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Detection and prevention of faults in overhead electric lines is critical for the reliability and availability of electricity supply. The disadvantages of conventional methods range from cumbersome installations to costly maintenance and from lack of adaptability to hazards for human operators. Thus, transmission inspections based on unmanned aerial vehicles (UAV) have been attracting the attention of researchers since their inception. This article provide a comprehensive review for the development of UAV technologies in the overhead electric power lines patrol process for monitoring and identifying faults, explores its advantages, and realizes the potential of the aforementioned method and how it can be exploited to avoid obstacles, especially when compared with the state-of-the-art mechanical methods. The review focuses on the development of advanced Learning Control strategies for higher manoeuvrability of the quadrotor. It also explores a suitable recharging strategies and motor control for improved mission autonomy.

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“…It is essential to carry out preventive monitoring of high voltage transmission lines more safely and efficiently in order to meet consumer demand [1][2][3]. UAVs have the potential to perform these monitoring tasks, due to their inherent advantages of cost, manoeuvrability, speed, and easy set-up [4,5]. They can attain the required heights and positions that are needed for performing major inspection tasks and do not require contact.…”

Section: Introductionmentioning

confidence: 99%

Application of Norm Optimal Iterative Learning Control to Quadrotor Unmanned Aerial Vehicle for Monitoring Overhead Power System

2020

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Wind disturbances and noise severely affect Unmanned Aerial Vehicles (UAV) when monitoring and finding faults in overhead power lines. Accordingly, we propose repetitive learning as a new solution for the problem. In particular, the performance of Iterative Learning Control (ILC) that are based on optimal approaches are examined, namely (i) Gradient-based ILC and (ii) Norm Optimal ILC. When considering the repetitive nature of fault-finding tasks for electrical overhead power lines, this study develops, implements and evaluates optimal ILC algorithms for a UAV model. Moreover, we suggest attempting a learning gain variation on the standard optimal algorithms instead of heuristically selecting from the previous range. The results of both simulations and experiments of gradient-based norm optimal control reveal that the proposed ILC algorithm has not only contributed to good trajectory tracking, but also good convergence speed and the ability to cope with exogenous disturbances such as wind gusts.

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Direct Sensing of Thrust and Velocity for a Quadrotor Rotor Array

Cited by 24 publications

References 7 publications

Supervisory Control of Multirotor Vehicles in Challenging Conditions Using Inertial Measurements

Supervisory Control of Multirotor Vehicles in Challenging Conditions Using Inertial Measurements

An Advanced Unmanned Aerial Vehicle (UAV) Approach via Learning-Based Control for Overhead Power Line Monitoring: A Comprehensive Review

Application of Norm Optimal Iterative Learning Control to Quadrotor Unmanned Aerial Vehicle for Monitoring Overhead Power System

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