Machine Learning with Echo State Networks for Automated Fault Diagnosis in Small Unmanned Aircraft Systems

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Abstract

Echo State Network (ESN) is one of machine learning methods that can be used to detect anomalies in sensor readings. The method predicts output signals, from which a prediction error can be created. To enable fault-tolerant control, ESN needs to be combined with a robust fault estimation method. Indeed, identifying the source of the faults, whether coming from sensors or actuators, is crucial in safety-critical Unmanned Aircraft Systems (UAS), since it will determine proper control actions when the faults occur. This paper presents a novel method to combine sensor anomaly detection using ESN with actuator fault estimation using adaptive extended Kalman filter (AEKF). Numerical results show the benefit of using the cascaded algorithm in a noisy environment. Furthermore, the presented method is validated using a hexacopter with actuator faults in indoor experiments.
Original languageEnglish
Title of host publication2022 International Conference on Unmanned Aircraft Systems (ICUAS)
Number of pages7
PublisherIEEE
Publication dateJul 2022
Pages1066-1072
ISBN (Electronic)978-1-6654-0593-5
DOIs
Publication statusPublished - Jul 2022
Event2022 International Conference on Unmanned Aircraft Systems, ICUAS 2022 - Dubrovnik, Croatia
Duration: 21. Jun 202224. Jun 2022

Conference

Conference2022 International Conference on Unmanned Aircraft Systems, ICUAS 2022
Country/TerritoryCroatia
CityDubrovnik
Period21/06/202224/06/2022
SeriesProceedings of International Conference on Unmanned Aircraft Systems
ISSN2575-7296

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

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