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

Husam A. Foudeh, Patrick Luk and James Whidborne
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Husam A. Foudeh: Electric Power and Drives Group, Cranfield University, Cranfield MK43 0AL, UK
Patrick Luk: Electric Power and Drives Group, Cranfield University, Cranfield MK43 0AL, UK
James Whidborne: Centre for Aeronautics, Cranfield University, Cranfield MK43 0AL, UK

Energies, 2020, vol. 13, issue 12, 1-16

Abstract: 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.

Keywords: unmanned aerial vehicles (UAVs); quadrotor; Iterative Learning Control (ILC); Norm Optimal ILC; gradient-based ILC; power system; inspection task (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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