Robust actuator fault diagnosis algorithm for autonomous hexacopter UAVs

Antonio Gonzalez Rot; Agus Hasan; Poramate Manoonpong

doi:10.1016/j.ifacol.2020.12.815

Robust actuator fault diagnosis algorithm for autonomous hexacopter UAVs

Antonio Gonzalez Rot^*, Agus Hasan^*, Poramate Manoonpong^*

^*Kontaktforfatter

Syddansk Universitet

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › peer review

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Abstract

This paper presents a robust actuator fault diagnosis algorithm for hexacopter Unmanned Aerial Vehicles (UAVs). The algorithm, based on Adaptive eXogenous Kalman Filter (AXKF), consists of two-stage operations: (i) a nonlinear observer and (ii) a linearized adaptive Kalman filter. To this end, we provide a sufficient condition for the nonlinear observer and recursive formulas for the linearized adaptive Kalman filter. The algorithm is tested for actuator fault diagnosis of a hexacopter UAV. Simulation results show that the proposed cascaded algorithm is able to accurately estimate the magnitude of the actuator fault.

Originalsprog	Engelsk
Bogserie	IFAC-PapersOnLine
Vol/bind	53
Udgave nummer	2
Sider (fra-til)	682-687
ISSN	2405-8971
DOI	https://doi.org/10.1016/j.ifacol.2020.12.815
Status	Udgivet - 2020
Begivenhed	21st IFAC World Congress 2020 - Berlin, Tyskland Varighed: 12. jul. 2020 → 17. jul. 2020

Konference

Konference	21st IFAC World Congress 2020
Land/Område	Tyskland
By	Berlin
Periode	12/07/2020 → 17/07/2020

Bibliografisk note

Funding Information:
This work was supported by the Free the Drones (FreeD) project, partly funded by Innovation Fund Denmark, at Center for Unmanned Aircraft Systems, M?rsk Mc-Kinney Moller Institute, University of Southern Denmark, project number 5156-00008B.

Publisher Copyright:
Copyright © 2020 The Authors.

Dokumenter og links

10.1016/j.ifacol.2020.12.815Licens: CC BY-NC-ND

Open Access VersionForlagets udgivne version, 2,78 MBLicens: CC BY-NC-ND

SDU link

Tjek adgangen til materialet i Syddansk Universitetsbiblioteks søgemaskine

Citationsformater

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title = "Robust actuator fault diagnosis algorithm for autonomous hexacopter UAVs",

abstract = "This paper presents a robust actuator fault diagnosis algorithm for hexacopter Unmanned Aerial Vehicles (UAVs). The algorithm, based on Adaptive eXogenous Kalman Filter (AXKF), consists of two-stage operations: (i) a nonlinear observer and (ii) a linearized adaptive Kalman filter. To this end, we provide a sufficient condition for the nonlinear observer and recursive formulas for the linearized adaptive Kalman filter. The algorithm is tested for actuator fault diagnosis of a hexacopter UAV. Simulation results show that the proposed cascaded algorithm is able to accurately estimate the magnitude of the actuator fault.",

keywords = "Fault diagnosis, Kalman filter, Unmanned Aerial Vehicles (UAVs)",

author = "Rot, {Antonio Gonzalez} and Agus Hasan and Poramate Manoonpong",

note = "Funding Information: This work was supported by the Free the Drones (FreeD) project, partly funded by Innovation Fund Denmark, at Center for Unmanned Aircraft Systems, M?rsk Mc-Kinney Moller Institute, University of Southern Denmark, project number 5156-00008B. Publisher Copyright: Copyright {\textcopyright} 2020 The Authors.; 21st IFAC World Congress 2020 ; Conference date: 12-07-2020 Through 17-07-2020",

year = "2020",

doi = "10.1016/j.ifacol.2020.12.815",

language = "English",

volume = "53",

pages = "682--687",

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AU - Rot, Antonio Gonzalez

AU - Hasan, Agus

AU - Manoonpong, Poramate

N1 - Funding Information: This work was supported by the Free the Drones (FreeD) project, partly funded by Innovation Fund Denmark, at Center for Unmanned Aircraft Systems, M?rsk Mc-Kinney Moller Institute, University of Southern Denmark, project number 5156-00008B. Publisher Copyright: Copyright © 2020 The Authors.

PY - 2020

Y1 - 2020

N2 - This paper presents a robust actuator fault diagnosis algorithm for hexacopter Unmanned Aerial Vehicles (UAVs). The algorithm, based on Adaptive eXogenous Kalman Filter (AXKF), consists of two-stage operations: (i) a nonlinear observer and (ii) a linearized adaptive Kalman filter. To this end, we provide a sufficient condition for the nonlinear observer and recursive formulas for the linearized adaptive Kalman filter. The algorithm is tested for actuator fault diagnosis of a hexacopter UAV. Simulation results show that the proposed cascaded algorithm is able to accurately estimate the magnitude of the actuator fault.

AB - This paper presents a robust actuator fault diagnosis algorithm for hexacopter Unmanned Aerial Vehicles (UAVs). The algorithm, based on Adaptive eXogenous Kalman Filter (AXKF), consists of two-stage operations: (i) a nonlinear observer and (ii) a linearized adaptive Kalman filter. To this end, we provide a sufficient condition for the nonlinear observer and recursive formulas for the linearized adaptive Kalman filter. The algorithm is tested for actuator fault diagnosis of a hexacopter UAV. Simulation results show that the proposed cascaded algorithm is able to accurately estimate the magnitude of the actuator fault.

KW - Fault diagnosis

KW - Kalman filter

KW - Unmanned Aerial Vehicles (UAVs)

U2 - 10.1016/j.ifacol.2020.12.815

DO - 10.1016/j.ifacol.2020.12.815

M3 - Conference article

AN - SCOPUS:85090387238

SN - 2405-8971

VL - 53

SP - 682

EP - 687

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 2

T2 - 21st IFAC World Congress 2020

Y2 - 12 July 2020 through 17 July 2020

ER -

Robust actuator fault diagnosis algorithm for autonomous hexacopter UAVs

Abstract

Konference

Bibliografisk note

Dokumenter og links

SDU link

Fingeraftryk

Citationsformater