Preliminary study of actuator fault detection for RUAVs using neuro-fuzzy system
With the ever-increasing use of Rotary Unmanned Aerial Vehicles (RUAVs) for various purposes, a fast and accurate fault detection system is key to detect unknown faults, to ensure fault tolerance and for safe operations. In this paper, a data-driven fault detection algorithm based on an Adaptive Neu...
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| Main Authors: | , , |
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| 其他作者: | |
| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
2021
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/147380 |
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| 總結: | With the ever-increasing use of Rotary Unmanned Aerial Vehicles (RUAVs) for various purposes, a fast and accurate fault detection system is key to detect unknown faults, to ensure fault tolerance and for safe operations. In this paper, a data-driven fault detection algorithm based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to detect single actuator faults of quadrotor flights through sensor readings from inertial measurement units. Sensor readings were collated and pre-processed to generate a training dataset to train the neuro-fuzzy model, and then the trained model was evaluated using four-fold cross-validation and a set of performance metrics. The results presented illustrate the generalisation performance ANFIS model and its effectiveness in detecting actuator faults. |
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