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Remaining Useful Battery Life Prediction for UAVs based on Machine Learning
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-7631-002X
Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-9701-4203
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-0126-1897
2017 (English)In: IFAC-PapersOnLine, ISSN 1045-0823, E-ISSN 1797-318X, Vol. 50, no 1, p. 4727-4732Article in journal (Refereed) Published
Abstract [en]

Unmanned Aerial Vehicles are becoming part of many industrial applications. The advancements in battery technologies played a crucial part for this trend. However, no matter what the advancements are, all batteries have a fixed capacity and after some time drain out. In order to extend the flying time window, the prediction of the time that the battery will no longer be able to support a flying condition is crucial. This in fact can be cast as a standard Remaining Useful Life prognostic problem, similarly encountered in many fields. In this article, the problem of Remaining Useful Life estimation of a battery, under different flight conditions, is tackled using four machine learning techniques: a linear sparse model, a variant of support vector regression, a multilayer perceptron and an advanced tree based algorithm. The efficiency of the overall proposed machine learning techniques, in the field of batteries prognostics, is evaluated based on multiple experimental data from different flight conditions.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 50, no 1, p. 4727-4732
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-66196DOI: 10.1016/j.ifacol.2017.08.863Scopus ID: 2-s2.0-85031802665OAI: oai:DiVA.org:ltu-66196DiVA, id: diva2:1150694
Conference
20th IFAC World Congress, Toulouse, France, 9-14 July 2017
Projects
Collaborative Aerial Robotic Workers, AEROWORKSIntegrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIRE
Funder
EU, Horizon 2020, 644128EU, Horizon 2020, 636834
Note

Konferensartikel i tidskrift

Available from: 2017-10-19 Created: 2017-10-19 Last updated: 2018-05-29Bibliographically approved
In thesis
1. On Visual Area Coverage Using Micro Aerial Vehicles
Open this publication in new window or tab >>On Visual Area Coverage Using Micro Aerial Vehicles
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The aim of this Licentiate is to advance the field of cooperative visual coverage path planners for multiple Micro Aerial Vehicles (MAVs), while aiming for their real life adoption towards the tasks of aerial infrastructure inspection. The fields that will be addressed are focusing in: a) the collaborative perception of the environment, b) the collaborative visual inspection, and c) the optimization of the aerial missions based on the remaining flying battery, camera constraints, coverage constraints and other real life mission induced constraints.

Towards this envisioned aim, this Licentiate will present the following main theoretical contributions: a) centralized and distributed Model Predictive Control (MPC) schemes for the cooperative motion control of MAVs focusing in the establishing of a formation control architecture to enable a dynamic visual sensor from monocular cameras towards a reconfigurable environmental perception, b) revisiting the Cooperative Coverage Path Planning (C-CPP) problem for the inspection of complex infrastructures, c) developing a holistic approach to the problems of 2-D area coverage with MAVs for polygon areas, while considering the camera footprint, and d) designing of a scheme to estimate the Remaining Useful Life (RUL) of the battery during a flight mission, a fact that directly effects the flying capabilities of the MAVs. The theoretical contributions of this thesis have been extensively evaluated in simulation and real life large scale field trials, a direction that adds another contribution of the suggested framework towards the massive insertion of the aerial platforms as aerial tools in the close future.

In the first part of this Licentiate, the vision, motivation, open challenges, contributions, and future works are discussed, while in the second part the full articles connected to the presented contributions in this Licentiate are presented in the annex.

Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2018
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-68666 (URN)978-91-7790-140-2 (ISBN)978-91-7790-141-9 (ISBN)
Presentation
2018-06-15, A1547, Luleå tekniska universitet, Luleå, 13:00 (English)
Opponent
Supervisors
Available from: 2018-05-07 Created: 2018-05-07 Last updated: 2018-06-08Bibliographically approved

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Mansouri, Sina SharifGeorgoulas, GeorgiosNikolakopoulos, George

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