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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Bibliographic Details
Main Authors: Husam A. Foudeh, Patrick Luk, James Whidborne
Format: Article
Language:English
Published: MDPI AG 2020-06-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/13/12/3223
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spelling 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
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