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 language | English |
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Title of host publication | 2022 International Conference on Unmanned Aircraft Systems (ICUAS) |
Number of pages | 7 |
Publisher | IEEE |
Publication date | Jul 2022 |
Pages | 1066-1072 |
ISBN (Electronic) | 978-1-6654-0593-5 |
DOIs | |
Publication status | Published - Jul 2022 |
Event | 2022 International Conference on Unmanned Aircraft Systems, ICUAS 2022 - Dubrovnik, Croatia Duration: 21. Jun 2022 → 24. Jun 2022 |
Conference
Conference | 2022 International Conference on Unmanned Aircraft Systems, ICUAS 2022 |
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Country/Territory | Croatia |
City | Dubrovnik |
Period | 21/06/2022 → 24/06/2022 |
Series | Proceedings of International Conference on Unmanned Aircraft Systems |
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ISSN | 2575-7296 |
Bibliographical note
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