Application of Norm Optimal Iterative Learning Control to Quadrotor Unmanned Aerial Vehicle for Monitoring Overhead Power System
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 o...
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Online Access: | https://www.mdpi.com/1996-1073/13/12/3223 |
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doaj-d48dfab1b5aa421d8e54581db9fe785c2020-11-25T03:45:16ZengMDPI AGEnergies1996-10732020-06-01133223322310.3390/en13123223Application of Norm Optimal Iterative Learning Control to Quadrotor Unmanned Aerial Vehicle for Monitoring Overhead Power SystemHusam A. Foudeh0Patrick Luk1James Whidborne2Electric Power and Drives Group, Cranfield University, Cranfield MK43 0AL, UKElectric Power and Drives Group, Cranfield University, Cranfield MK43 0AL, UKCentre for Aeronautics, Cranfield University, Cranfield MK43 0AL, UKWind 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.https://www.mdpi.com/1996-1073/13/12/3223unmanned aerial vehicles (UAVs)quadrotorIterative Learning Control (ILC)Norm Optimal ILCgradient-based ILCpower system |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Husam A. Foudeh Patrick Luk James Whidborne |
spellingShingle |
Husam A. Foudeh Patrick Luk James Whidborne Application of Norm Optimal Iterative Learning Control to Quadrotor Unmanned Aerial Vehicle for Monitoring Overhead Power System Energies unmanned aerial vehicles (UAVs) quadrotor Iterative Learning Control (ILC) Norm Optimal ILC gradient-based ILC power system |
author_facet |
Husam A. Foudeh Patrick Luk James Whidborne |
author_sort |
Husam A. Foudeh |
title |
Application of Norm Optimal Iterative Learning Control to Quadrotor Unmanned Aerial Vehicle for Monitoring Overhead Power System |
title_short |
Application of Norm Optimal Iterative Learning Control to Quadrotor Unmanned Aerial Vehicle for Monitoring Overhead Power System |
title_full |
Application of Norm Optimal Iterative Learning Control to Quadrotor Unmanned Aerial Vehicle for Monitoring Overhead Power System |
title_fullStr |
Application of Norm Optimal Iterative Learning Control to Quadrotor Unmanned Aerial Vehicle for Monitoring Overhead Power System |
title_full_unstemmed |
Application of Norm Optimal Iterative Learning Control to Quadrotor Unmanned Aerial Vehicle for Monitoring Overhead Power System |
title_sort |
application of norm optimal iterative learning control to quadrotor unmanned aerial vehicle for monitoring overhead power system |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2020-06-01 |
description |
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. |
topic |
unmanned aerial vehicles (UAVs) quadrotor Iterative Learning Control (ILC) Norm Optimal ILC gradient-based ILC power system |
url |
https://www.mdpi.com/1996-1073/13/12/3223 |
work_keys_str_mv |
AT husamafoudeh applicationofnormoptimaliterativelearningcontroltoquadrotorunmannedaerialvehicleformonitoringoverheadpowersystem AT patrickluk applicationofnormoptimaliterativelearningcontroltoquadrotorunmannedaerialvehicleformonitoringoverheadpowersystem AT jameswhidborne applicationofnormoptimaliterativelearningcontroltoquadrotorunmannedaerialvehicleformonitoringoverheadpowersystem |
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