ICUAS'22 Paper Abstract

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Paper FrA2.6

Diget, Emil Lykke (University og Southern Denmark), Hasan, Agus (Norwegian University of Science and Technology), Manoonpong, Poramate (The Maersk Mc-Kinney Moller Institute, University of Southern De)

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

Scheduled for presentation during the Regular Session "Environmental and Safety Issues" (FrA2), Friday, June 24, 2022, 10:40−11:00, Bokar

2022 International Conference on Unmanned Aircraft Systems (ICUAS), June 21-24, 2022, Dubrovnik, Croatia

This information is tentative and subject to change. Compiled on March 29, 2024

Keywords Fail-Safe Systems, Reliability of UAS, Manned/Unmanned Aviation

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.

 

 

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